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Analytical study on the impact of technology in higher education during the age of COVID-19: Systematic literature review

• Published: 30 March 2021
• Volume 26 , pages 6719–6746, ( 2021 )

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• Manar Abu Talib   ORCID: orcid.org/0000-0003-3001-0077 1 ,
• Anissa M. Bettayeb 1 &
• Razan I. Omer 1  

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With the advent of COVID-19 arose the need for social distancing measures, including the imposition of far-reaching lockdowns in many countries. The lockdown has wreaked havoc on many aspects of daily life, but education has been particularly hard hit by this unprecedented situation. The closure of educational institutions brought along many changes, including the transition to more technology-based education. This is a systematic literature review that seeks to explore the transition, in the context of the pandemic, from traditional education that involves face-to-face interaction in physical classrooms to online distance education. It examines the ways in which this transition has impacted academia and students and looks at the potential long-term consequences it may have caused. It also presents some of the suggestions made by the studies included in the paper, which may help alleviate the negative impact of lockdown on education and promote a smoother transition to online learning.

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1 Introduction

Severe acute respiratory syndrome, also known as COVID-19, is a contagious respiratory disease caused by the SARS-CoV-2 virus, which was first identified in a seafood market in Wuhan in late December 2019 (Huang, 2020 ).

The disease is airborne and mainly spreads through physical proximity with infected people. Clinical analysis results of the virus showed person-to-person transmission (Li et al., 2020 ). Broadly speaking, there are two modes of transmission—direct and indirect. The direct mode involves droplet and air transmission, while indirect transmission may occur via contaminated surfaces (Karia, 2020 ).

Due to its highly contagious nature, the COVID-19 virus swept the globe in the matter of weeks. Between December 2019 and October 2020, more than 45 million cases of COVID-19 were reported, including over a million deaths. (European Centre for Disease Prevention and Control, 2020 ). By March 2020, the epidemic was declared a pandemic by the World Health Organization (WHO, 2020 ).

The call for social distancing and limiting face-to-face contact outside the immediate family has never been louder. Social distancing is a deliberate increase in the physical gap between individuals to minimize the spread of disease (Red Cross, 2020 ).

Many facets of everyday life have been devastated by the pandemic. It prompted counties around the world to adopt a sequence of emergency response systems (Zhang et al., 2020 ). Authorities worldwide issued stay-at-home orders, imposing prolonged periods of lockdown, which led to a disruption in educational activities globally. This was done to curb infection rates and flatten the incidence curve in an effort to prevent healthcare systems from being overwhelmed.

In many parts of the world, this meant a temporary shutdown of educational institutions. These nationwide closures impacted millions of students and their families, particularly those from underprivileged communities (UNESCO, 2020 ).

Some of the educational institutions that faced closure progressively re-opened and started operating under online learning models in order to continue the academic progress of students, while simultaneously observing measures to reduce the impact of the current health crisis.

Previous outbreaks of infectious diseases such as swine flu have prompted significant school closures worldwide, with varying degrees of effectiveness (Barnum, 2020 ). If school closures happen late during a pandemic, they are less effective and may have little impact at all (Zumla et al., 2010 ). Educational institutions have been compelled to make an immediate transition to remote methods of learning that rely heavily on technology. The immediate transition to online learning has not made it possible for many to be adequately prepared for the challenges ahead (Hodges et al., 2020 ). This migration to remote learning had to be implemented as quickly as possible, and for many learning institutions, it happened several months into the academic year, leaving both staff and students with little time to plan, adjust and adapt.

This shed the light on various underlying economic and social issues. According to UNESCO, more than a billion learners worldwide have been affected at some point by the school closures that were initiated in response to the COVID-19 pandemic. As of November 2020, over 300 million learners spread across over 30 countries, which constitute approximately 18% of total enrolled learners, have been kept out of schools due to lockdown (UNESCO, 2020 ).

While the disruption in learning caused by COVID-19 is unprecedented, important insights can be gained about its far-reaching implications through an examination of relevant existing studies and data.

This paper is a systematic literature review that looks at the existing literature and discusses the crisis-response migration methods to technology-based online learning done by mainly higher learning institutions in terms of their impact on instructional delivery, students and faculty, and education as a whole. Firstly, it categorizes the studies in terms of which facet of education the impact of COVID-19 in was explored. Then, it performs a SWOT analysis on the digital transformation to online learning. In other words, it looks at the strengths, weaknesses, opportunities and threats. Lastly, it attempts to collect and summarize student and faculty feedback on online education and then outlines some of the recommendations made by either the students and faculty or the authors of the selected studies for improving the system.

The rest of the paper is divided into six sections. Section 2 discusses some of the related works, while Section 3 presents the methodology used in this study, including the selected research questions, search strategy, study selection process, quality assessment rules and data extraction strategy. It also presents some statistics about the selected papers. Section 4 presents the findings of the study and discusses them in detail, while Section 5 concludes with a summary of the research outcomes and possible future work. Section 6 constitutes an acknowledgement of various contributions to the creation of this paper.

2 Related work

In response to COVID-19, a lot of countries were faced with pressure to contain the spread of this highly contagious disease. To many educational institutions, this meant either partial or complete closure. Others transitioned to technology-based distance learning.

A systematic review was conducted by Viner et al. ( 2020 ) examines existing knowledge to identify the effects of school closures and other social distancing measures during outbreaks on infection rates and virus transmission. It suggested that school closures play a relatively small role in the control of disease transmission, and that the insignificant benefits such closures bring to transmission reduction could be easily outweighed by their profound negative economic and social consequences (Viner et al., 2020 ).

There is no strong evidence to support the effectiveness of full closure in controlling the pandemic. If anything, there are significant economic downfalls to such a response, not to mention the academic delay incurred by students. That is why a lot of academic institutions opted for the less drastic measure of transitioning to online distance education (ODE).

ODE is the use of the internet and certain other significant technology for the production of educational content, instructional delivery and program management (Fry, 2001 ). ODE can be delivered in two main formats: synchronous and asynchronous. As the name suggests, synchronous distance education (SDE) involves live, real-time interaction between teachers and students. It aims to simulate the communication model of a traditional classroom. Examples of SDE would include live webinars or virtual classrooms. Asynchronous education, on the other hand, introduces temporal flexibility. It does not require real-time interaction; instead, the educational material is available online for students to access at their own convenience. Examples of asynchronous education would be video recordings and emails (The Florida Center for Instructional Technology, n.d. ).

A systematic review and meta-analysis provided on randomized controlled trials (RCTs) conducted by papers released between January 2000 and March 2020 on the effectiveness and acceptance of SDE in health sciences as compared to more traditional educational methods measured the knowledge of students, their skills (using objective assessments) and their overall satisfaction (using subjective evaluations). It found there to be no significant difference between traditional education and synchronous distance education in terms of effectiveness and objective assessments. However, in subjective evaluations, SDE resulted in a higher satisfaction rating, indicating that it was preferred to some extent by students, despite being neither better nor worse in the earlier two measures (He et al., 2020 ).

Additionally, Carrillo & Flores ( 2020 ) conducted a review of the literature between January 2000 and April 2020 on online teaching and learning practices in teacher education to explore how and why online teaching and learning in teacher education occur, and also discussing its implications in the context of the pandemic. The review highlighted the complex nature of the model, discussing such factors as social, cognitive and teaching issues and the need for a comprehensive view of the pedagogy of online technology-based education used to support teaching and learning (Carrillo & Flores, 2020 ).

Daoud et al. ( 2020 ) conducted a systematic review focused on the issue of equity regarding home internet access by evaluating the educational value of having internet at home for school-aged children. It found a range of correlations that were mostly positive between access to home internet and educational value across three functions: qualification (academic knowledge and skills), subjectification (strengthening individuality) and socialization (of future citizens). However, the correlation was not straightforward, nor did it imply causation. The educational value in home internet use is influenced by variables regarding the nature of online activities such as how the technology is being used and socio-economic status (Daoud et al., 2020 ).

Di Pietro et al. ( 2020 ) produced a paper that attempts to explore the direct and indirect ways in which the COVID-19 pandemic may impact education. Based on the existing literature and pre-COVID-19 data, it made predictions about the impact on and future of education. The paper drew four main conclusions: 1) learning is expected to suffer a setback on average; 2) the effect on academic performance is likely to vary with socio-economic status; 3) inequality in socio-economic status may manifest in an emotional response, as those from less privileged backgrounds may be under more environmental stress; 4) the widening social gap may persist and have long-term implications (Di Pietro et al., 2020 ).

Some online emergency learning approaches are criticized for not adhering to sound pedagogical norms, best practices and prior studies (Hodges et al., 2020 ). Some have noted the potential negative effects of educational technology fixes being implemented quickly without balancing their effect (Selwyn et al., 2020 ; St. Amour, 2020 ). In addition, leaping into online education and online learning platforms has also raised concerns regarding surveillance and privacy and its impact on the lives of students (Harwell, 2020 ).

A study that aims to map the scientific literature in the areas of education and management in the context of the COVID-19 pandemic suggests the existence of three distinct groups or research flows in the published literature. These main themes were identified as: 1) education based on online constructs and distance learning; 2) the impact of COVID-19 from a management perspective; and 3) studies with a particular focus on Canada. The studies chosen for the analysis were found to be of various typologies, the most relevant of which was qualitative. The analysis revealed that research on the disruption in education and scientific production caused by the pandemic is rather scarce, which might be the result of the lack of empirical data (Rodrigues et al., 2020 ).

Since this phenomenon is still relatively recent, there is a lack of research that discusses the direct effect of the digital transformation in higher education caused by the pandemic, its pros, cons and future implications. This systematic literature review is different from those described above, as it provides an extensive review on the research done on the impact of the COVID-19 pandemic on formal education. Specifically, this study explores the ways in which the transition from traditional in-person educational models that involve face-to-face interaction and classroom teaching to ODE has impacted academia and students, and the consequences it might have had on student performance and the well-being of all involved.

The pandemic might have set in motion changes that are to last millennia in the way education is conducted across the globe. It is therefore imperative to study the direct impact of the pandemic on the education sector and understand the role it played in revolutionizing the way we think about education in order to make informed pedagogical choices in the future and ensure a smooth transition into more flexible but effective online teaching methods. As a result, our research paper has the following important contributions:

Explore the kind of changes the shift to online education has caused

Discuss the impact of these changes on students and teachers

Provide an insight into the current state of education and how the pandemic could affect its future

Table  1 summarizes the literature reviews discussed in this section as well as this study’s objective.

3 Methodology

This study is a Systematic Literature Review (SLR) based on the guidelines for performing such reviews laid out in the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (Moher et al., 2015 ), which are comprised of three main stages: search, eligibility and data collection and extraction. For example:

Search defines the search strategy in terms of what keywords and search engines or libraries will be used.

Eligibility is concerned with setting up inclusion and exclusion criteria aligned with the research objectives to specify the study and reporting standards, and then applying them to the collected papers.

Data collection and extraction is the process of obtaining eligible reports and extracting data from them in order to investigate the posed research questions.

This study tackles the topic of education during the COVID-19 pandemic and the accompanying shift to remote learning. The review process is composed of six stages. The first stage was coming up with research questions that reflect the aim of the study. The second involved collecting papers relevant to the topic. In the third stage, exclusion and inclusion criteria were defined and applied to the collected papers. The fourth stage involved extracting answers to the research questions from papers that made it through the final round of exclusion. The fifth and final stage was the synthesis of data obtained through this information extraction process to reach meaningful conclusions.

Figure  1 below illustrates this process.

Research methodology

3.1 Research questions

This systematic literature review aims to examine and summarize the impact COVID-19 had on education through the shift to online learning it caused in early 2020. The following five research questions were posed:

What are the aspects and impacts of COVID-19 on education?

RQ1 aims to identify the underlying theme or lens through which the impact of COVID-19 on education was explored in the papers. In other words, on what aspect of education or the educational system is the paper attempting to shed light on the impact of COVID-19?

What are the limitations of online education?

RQ2 examines the implemented online teaching models critically and identifies their flaws as defined in the research papers. This is the first phase of a SWOT analysis, which stands for strengths, weaknesses, opportunities, and threats. It considers the weakness and threats of online education.

What are the advantages & opportunities laid out by this digital transformation in higher education?

The aim of RQ3 is to recognize the benefits and opportunities presented by this unprecedented move toward digital-based learning in higher education institutions. This is the second phase of the SWOT analysis and it focuses on the digital transformation’s strengths and opportunities.

What was the feedback of students and teachers?

RQ4 collects and summarizes the responses of students and teachers to this transformation and how it impacted their experience.

What recommendations were made?

RQ5 attempts to summarize the recommendations put forward by either the authors of the studies or the people who participated in them.

3.2 Search strategy

The research questions were used as a guideline to roughly identify the main search keywords. Terms synonymous or highly related to the main search keywords were included in the search. Google Scholar was used for the search, which employed variations of the following search keywords: “COVID-19” “effects” “impact” “education” “higher education” “academia” “university” “online learning” “students” “teaching” “e-learning”.

The number of results varied by combination of keywords, but on average between 200 and 300 results showed up per search, a number increasing by the day given the current relevance of the topics at hand. The majority of papers came from journals.

3.3 Study selection

All papers based on the search keywords mentioned above that seemed, if only tenuously, relevant to the topic of education during COVID-19 were collected. Only papers that were published later than 2019 were retained. Papers that did not belong to high-quality, prestigious journals were excluded.

To ensure the quality of the selected papers and they do not belong to predatory journals, we first checked them against Elsevier’s abstract and citation database, Scopus. We also made sure they belonged to either the first quartile (Q1) or second quartile (Q2) according to the SCImago Journal Rank (SJR). SJR indicates the scientific influence of scholarly journals. Moreover, the journals were reviewed against Beall’s List, which is a list of predatory open-access publishers that did not perform proper peer review and they publish any article as long as the authors paid the open-access fee. This brought the number of papers selected for the purpose of this study down dramatically to 47—less than half of all papers collected initially.

As mentioned earlier, the search based on the selected keywords yielded somewhere between 100 and 300 results. Over 100 papers seemed relevant and were downloaded to serve as a starting point. Moving on, we filtered the papers based on their compliance with our inclusion criteria. The process can be summarized as follows:

download papers that showed up in the search results

delete any duplicates

apply the inclusion and exclusion criteria to get rid of any irrelevant papers

set aside survey and review papers

extract answers to the research questions from the selected papers while applying the quality assessment rules stated in section 3.4 that were designed to include only qualified papers.

Table  2 summarizes the applied inclusion and exclusion criteria of study papers.

3.4 Quality assessment rules (QARs)

This final step is to determine the quality of the collected research papers. To measure the quality of the papers included in the study and confirm their pertinence to our research objectives, ten Quality Assessment Rules (QARs) were set. Marks out of 10 were given to each paper based on its compliance with the established QARs. The QARs were formulated based on our understanding of the current state of research in this field and the research gap this paper is attempting to fill. The papers were scored for their ability to meet high research standards while adequately addressing our research question. For each of the ten questions, a paper is given a score as follows: “fully answered” = 1, “above average” = 0.75, “average” = 0.5, “below average” = 0.25, “not answered” = 0. The summation of the marks achieved for the 10 QARs is the paper’s ranking. Papers that score 5 or higher are accepted, while the remaining are excluded.

Are the study objectives clearly defined?

Is the impact of COVID-19 on education well-defined?

Is the specific context and usage (themes) clearly defined?

Is the study method well-designed and justifiable?

Is the scope of the study large enough?

Are the advantages and opportunities of the proposed teaching/technology methods well-explained?

Are the weaknesses and limitations of the proposed teaching/technology methods well-explained?

Are student/teacher evaluations reported?

Are the recommendations of the proposed methods suitable?

Overall, does the study enrich the academic community or industry?

3.5 Data extraction strategy

In this step, the final list of papers was analyzed to answer the research questions and extract any pertinent information.

The following information was extracted from each paper: Paper title, Publisher, Journal, month of publication, description of the paper’s objective, the answers to RQ1, RQ2, RQ3, RQ4 and RQ5.

Due to the indistinct terminology used within some papers and the relative narrowness of our research questions in comparison to the questions posed by the collected papers, there were gaps in the answer extraction as reflected in Fig.  4 .

In some cases, the authors had to infer answers that weren’t explicity expressed in the papers. This meant that some of the answers extracted were personal intrepretations of the findings done by the authors.

3.6 Statistics about the selected papers

As can be seen from Fig. 2 , Elsevier & IJWIL journals held the 2nd and 3rd positions, coming in at 19% and 17% respectively. Other publishers, including Springer, Routledge & MDPI, contributed similar amounts of papers and came at 13% of the total paper count or less.

Publishers of the selected papers by frequency

However, 32% of the papers were put out by miscellaneous publishers. These publishers include: The BMJ, ACS Publication, Science Press, Wiley, Taylor and Francis Ltd., Primrose Hall Publishing Group, Scientific Research Publishing, Academy of Science of South Africa, Association for Learning Technology, Association for Social Studies Educators, Modestum and Kathmandu University.

Figure  3 shows the months of publication of the selected papers. It is noteworthy that the largest number of papers relevant to this review were produced in July, three to four months after many lockdowns were implemented and distance learning was put in effect. The number of papers selected for this review subsequently declined. For 13 of the selected papers, the month of publication was either not explicitly specified or couldn’t be identified by the authors.

Frequency of selected papers by month

As can be seen from Fig. 4 , all research questions were answered by more than 70% of the papers, which speaks to their broadness and generality. The only exception was RQ5, which had a 61.70% answer rate, mostly from papers discussing the topic of “student experience”, as will be shown in the following section.

Frequency of answers for each research question

4 Results and discussion

The majority of educational institutions in the chosen studies migrated to distance learning. While not all papers specified the particular methods or platforms employed, video conferencing, E-portals, webinars, websites, video recordings, simulations and online quizzes were frequently listed as the primary means of conducting classes and evaluating student performance.

A total of 47 studies were compiled using the quality criteria mentioned in section 3.4 . A list of these studies is included in Table  7 in Appendix A. Here in section 4 , we present the findings of this literature review. The outcomes of each research question are explored in detail in each of the following five sections.

4.1 Area of focus

In this section, the first research question (RQ1) is addressed, which aims to identify the underlying theme or lens through which the impact of COVID-19 on education was explored in the papers. There were four main identifiable themes:

Impact on Education : explores the transition from traditional classroom teaching methods to more technology-based learning, and the impact of that transition.

Student Experience : explores the impact the lockdown had on students either academically or personally and their experience with ODE as well as their academic performance using remote learning methods.

Proposal : proposes and/or experiments with a remote teaching method or platform.

Policy : explores the responses to the pandemic and the role of policymaking in leveling the playfield in education.

Equality : discusses the disparity observed between different social groups during the pandemic and the impact it had on accessibility and equity.

In this review, 25 papers discussed the impact of COVID-19 on education, namely the digital transformation driven by it, its advantages and disadvantages, and what this could mean going forward.

Eighteen papers included discussions about the experience of students and staff with ODE, as well as the participants’ views on its potential upsides and downsides. Most of the answers given for RQ5 came from this group.

Four papers proposed solutions for remote learning or experimented with a particular platform to analyze its efficacy.

Three papers looked at the current academic situation through a political lens, discussing education-related policy in light of the pandemic.

Two papers discussed how the lockdown and the accompanying transition to technology-based learning further exacerbated differences in educational progress between the children of lower income families with limited access to Wi-Fi and digital devices or services and those of higher income families that do not share the same struggles.

Figure  5 highlights the differences in the frequency of the discussed areas. It is worth noting that these percentages add up to more than 100% because there is overlap between the papers in terms of the areas chosen for discussion.

Topics discussed in selected papers

4.2 Disadvantages & limitations

This section addresses research question 2 (RQ2), which takes a critical view of the implemented teaching models and identifies their shortcomings as described in the papers that studied or mentioned them.

The key disadvantages can be summarized in the following points:

Inequality & inaccessibility : there is a gap in student access to this type of education, which is usually related to family income.. Transitioning to online learning exacerbated differences between privileged and underprivileged students. Students from less prosperous regions have limited or no access to digital devices and Wi-Fi. They also have lower technical abilities., granting more privileged students an unfair academic advantage. This disparity extends to educational institutions in rural areas or deprived parts of the world that may be less well-equipped than those in urban areas.

Inadequacy : while technology can be a great aid to the learning experience, it cannot act as a complete substitute, particularly for STEM fields that require hands-on training in laboratories or operation rooms. This is especially true for health care sciences. 34% of the chosen studies focused on medical education specifically, looking at nursing or residency programs in particular. These papers tended to emphasize the value of practical training and how indirect knowledge gained from simulations or demonstration videos alone cannot act as a substitute.

Communication quality : building and sustaining relationships and developing rapport between students, their peers, and their teachers became more difficult due to the devaluation or lack of face-to-face contact, as well as the inherent ambiguity of written interactions.. Clarifying instructions and gauging student response, engagement and participation, or lack thereof, becomes more difficult for teachers and instructors in the absence of direct contact and the ability to monitor students face-to-face.

Technical difficulties : poor internet reception or Wi-Fi, connection stability, glitches and other technical failures can interfere with the flow of communication.

Stress, workload and morale : the forced and rapid transition to online learning affected mental health among students. Many experienced lockdown-related anxieties about financial stability and socializing that indirectly affected their performance. Academic staff had to deal with an increased or even doubled workload. Also, lack of face-to-face social interaction for extended periods of time can have a detrimental effect on mental health.

Technological literacy : due to the sudden and forced nature of this digital transition, a lot of educational institutions were caught off-guard, allowing them little to no time to prepare their academic staff. This left non-tech savvy teachers and instructors underprepared and/or underequipped to handle sophisticated computer and internet related tasks. Instructors’ lack of technological competence and previous training in or familiarity with utilizing online tools posed an obstacle. The inability of academic staff to use technology negatively impacted the success of ODE in many cases.

Student engagement, participation and motivation : student engagement was sometimes lacking due to factors such as reliance on recorded lectures, a lack of motivation or interest, stress and boredom, as well as the distraction caused by using electronic devices. Added to this was fatigue induced by prolonged staring at screens and feelings of isolation and depression from lack of personal contact.

Student performance assessment : due to the difficulties associated with bringing students to campus to administer tests, academic staff were faced with the challenge of redesigning evaluations in a way that fairly and reliably captured student performance. This was particularly challenging in practical courses.

Work-life balance : ODE allows great flexibility in time and location. While this flexibility may be convenient, it’s a double-edged sword that could also blur the boundaries between academic and personal life. Whereas in conventional educational models lectures are strictly bound by fixed times and physical locations.

Privacy concerns : concerns about breach of privacy, data protection and anonymous misconduct.

Table  3 lists the research articles that mentioned disadvantages and limitations of distance education based on the aforementioned points.

4.3 Advantages & opportunities

This section addresses research question 3 (RQ3), which aims to identify the advantages and opportunities laid by this digital transformation in education.

There are several main identifiable key advantages and opportunities. They can be summarized as follows:

Remote learning : ODE transcends the borders of time and geographical location. It allows students the flexibility to tune in into their lectures from the comfort of their own homes or any other location. It also allows students to self-regulate their learning and proceed at their own pace thanks to the temporal flexibility of online learning, which is made possible by features such as lecture recording.

Discussion & Communication : online learning facilitates a modern and convenient mode of communication. Important discussions can be raised during lectures and participating students can benefit from these discussions by listening or by engaging through chat. It is also an effective means of communication as participants do not have to meet in person or face the discomfort that can accompany speaking in front of a live audience, thereby further encouraging discussion. Online learning also helps parents of young children to be more involved in their children’s education.

Impetus for change : this forced digital transformation in education exposed problems within the system and pushed educators to contemplate and review current and previous models of education, providing a window into what a technology-based education and work environment might be like, thereby stimulating pedagogical innovations and accelerating change. It is hastening progress and can be viewed as an impetus for the reform of curriculum and teaching approaches.

Equally effective : the implementation of online learning and the use of simulations and other methods for didactic purposes were perceived as useful and adequate, if not complete, substitutes for traditional learning. It accomplished its goal of continuing the delivery of education amidst the pandemic, while also helping students meet the requirements expected from them.

Efficient : contributed to or improved knowledge dissemination, with cost-effectiveness, flexibility and overall efficiency as added benefits.

Exposure to tech : incorporating technology into education exposes students to modern and relevant technologies. This helps both students and academic staff close the technological literacy gap while also fostering expertise in online and digital media, thereby preparing students for the job market in an increasingly technology-reliant world of digitization and automation.

Decreased costs : the shift to online education can be credited for the decrease in educational costs. It provides students with a comparable learning experience without the need for expensive infrastructure, not to mention a reduction in other hidden costs such as travel expenses.

Table  4 lists the research articles that mentioned advantages and opportunities of distance education based on the aforementioned points.

4.4 Student and teacher feedback

This section addresses the fourth research question (RQ4), which aims to gauge the response of students and teachers to this transformation and how it impacted their experience.

The papers that explored the topic of student experience provided the main insights to this question, which can be summarized as follows:

Satisfactory or beneficial : ODE was regarded as a good learning experience and helpful in the sense that it assisted in cultivating knowledge in a unique and efficient manner.

Adequate and effective : ODE was deemed satisfactory in achieving its objective of continuing education. In some cases, it was thought to have had no significant impact on studies. And in other cases it was thought to boost productivity.

Expressed doubts or concern : participants expressed doubts about the efficacy of ODE, uncertainty about the future, and concern over the long-term consequences of the digital transformation on health, security and equity..

Overwhelming : some staff had difficulty adjusting given how abrupt the transition was. Many had to devise new student performance assessment methods to compensate for the inability to directly monitor students in exams and quizzes. In some cases, the transition led to an increase in workload.

Potential : some participants thought ODE could support their teaching or studies, recommended it for future use or viewed it as a catalyst for revision of existing norms.

Appreciation for staff or peers : participants expressed appreciation and gratitude towards others within the institution for their efforts in coping with the situation, providing assistance and being responsive.

Improvement in performance : ODE was thought to enhance efficiency, performance and attention, as well as help in the learning process.

Preferred to traditional : although students expressed sentiments of missing peer-to-peer interaction, the majority were open to and some even favoured ODE to conventional learning that requires physical attendance and is restricted to classrooms. This may be due to the flexibility, convenience and low cost of online learning.

Anxiety inducing : some participants reported feelings of stress or anxiety in trying to grapple with the current pandemic situation while adapting to the new learning scheme.

Table  5 lists the research articles that described feedback received on distance education based on the aforementioned points.

4.5 Study recommendations

This section addresses research question 5 (RQ5), which attempts to summarize the recommendations put forward by either the authors of the selected studies or the people who participated in them.

The following are the key recommendations made:

Support for students : boosting and maintaining motivation of students to improve morale and help combat any lockdown-induced stress or anxiety.

High-quality tools : providing accessible, user-friendly, error-free and high-quality E-learning portals and other types of online platforms.

Providing & receiving feedback : providing and receiving feedback to and from students to improve the quality of online education.

Investigating efficacy : exploring the outcomes of ODE and reflecting on the differences between it and traditional education in order to ascertain which aspects of it are viable and meet the demands sets by the pandemic situation. This is also to assist teachers in employing effective teaching techniques and to enable researchers and institutions to continue the development of online educational tools.

Stating objectives : students need to feel the relevance of the study material to the real world, as well as understand the course requirements. To that end, teachers must spell out expectations and clarify course objectives as well as the importance of the syllabus. They also need to delineate their roles and responsibilities as lecturers and mentors early on in the academic year.

Policymaking : policymakers should seek to understand and mitigate any risks or inequalities created by this rapid transition to online learning, which may be caused by income or workload disparities.

Redesign : the revisiting and rethinking of pedagogical strategies and the development of orienting principles to guide the transition to online education, as well as making the necessary adjustments to infrastructure.

Training of staff & students : providing students and teachers with adaptability training to familiarize them with technology, increase their competence and prepare them to deal with technical issues that can occur during online lectures. This will also help in the smart application of technology to realize its potential in the realm of online education.

Diversifying : maximizing efficiency by avoiding reliance on a single method or platform and instead using a variety of online learning resources. For example, a course could use both video conferencing and text messaging.

Broadening accessibility : this could mean providing underequipped students with the equipment necessary to partake in online activities, such as electronic devices and stable internet connection.

Table  6 lists the research articles that made recommendations based on the points listed above.

5 Conclusion and future work

It goes with without saying that the COVID-19 pandemic has had profound impacts on society and on the way humans organize themselves in the real world. It has exposed systematic issues within institutions and brought about long overdue changes. The educational system was no exception to this.

This review aimed to look at and evaluate the impact these changes have had on education, with a particular focus on the digital transformation and the shift to online learning caused by the pandemic. To do so, we took a look at more than 40 papers from high impact journals that touched on the topic of education during the times of COVID-19.

Many institutions and governments were underprepared for this abrupt migration to technology-based working and learning. This resulted in issues of inequality, lack of access and lack of skills to facilitate this type of learning. There are limitations inherent to ODE that prevent it from acting as a full substitute to traditional education. This is particularly true in fields where hands-on training is an absolute necessity to meet learning requirements.

On the plus side, the new forced dependence on technology in education may hasten some already underway changes. On the negative side, requiring children to continue their studies at home may worsen educational disparities caused by inequalities.

From the viewpoint of learners and educators, there are a range of difficulties in switching from offline to online learning modes. Another stumbling block in the acceptance of online teaching is involving students and indulging them in teaching-learning progression. It takes an hour to create content that not only covers the curriculum, but also inspires learners.

We found that some of the key disadvantages of ODE that were cited in the collected papers were inequality of access, inadequacy of online teaching, poor communication quality, technical difficulties, increased workload and stress, low technological literacy, difficulty in assessment of student engagement and performance, bad work-life balance and some privacy concerns.

Whereas the main advantages of ODE according to the papers were flexibility and convenience, discussion & communication, effectiveness as a didactic tool, efficiency, decreased costs, increased exposure to technology and that it was seen as an impetus for change.

The papers that explored the topic of student experience aimed to gauge the response of students and teachers to this transformation and how it impacted their experience and we found that the main feedback point given was that online education was satisfactory, beneficial and effective. However, some expressed doubts over the efficacy of remote learning, uncertainty about the future, and concern over the long-term consequences on health, security and access due to this digital transformation. Others found it to be overwhelming or anxiety inducing. However, some observed an improvement in performance and expressed more appreciation towards their peers and faculty members.

Although the adoption of online teaching during COVID-19 is commendable, the quality of teaching and courses offered online must also be developed and strengthened. Some of the advice that has been put forward to help in that regard includes supporting students by improving morale, providing high-quality e-learning tools, giving and receiving feedback from students, investigating the outcomes of ODE, clarifying course objectives and expectations to students, providing training for students and teachers to familiarize them with technology, diversfying instructional delivery methods, broadening accessibility to online learning, soliciting policymakers to make necessary changes and the revisiting and redesigning of pedagogical strategies.

The flexibilty and convenience ODE offers and the much-needed push for change it has inspired cannot be denied. However, its efficiency in terms of student outcome as compared to traditional education is still a point of dispute. It is therefore imperative to continue investigating online education. Policymakers should take the findings of research on education seriously in order to bridge whatever gaps may be present.

Future research could draw from a broader diversity of sources to reach wider conclusions.

Data availability

The data is available to anyone for review.

Code availability

Not applicable.

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The authors would like to thank University of Sharjah and OpenUAE Research and Development Group for funding this research study. We are also grateful to our research assistants who helped in collecting, summarizing, and analyzing the research articles for this SLR study.

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Abu Talib, M., Bettayeb, A.M. & Omer, R.I. Analytical study on the impact of technology in higher education during the age of COVID-19: Systematic literature review. Educ Inf Technol 26 , 6719–6746 (2021). https://doi.org/10.1007/s10639-021-10507-1

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Technologies are becoming increasingly complicated and increasingly interconnected. Cars, airplanes, medical devices, financial transactions, and electricity systems all rely on more computer software than they ever have before, making them seem both harder to understand and, in some cases, harder to control. Government and corporate surveillance of individuals and information processing relies largely on digital technologies and artificial intelligence, and therefore involves less human-to-human contact than ever before and more opportunities for biases to be embedded and codified in our technological systems in ways we may not even be able to identify or recognize. Bioengineering advances are opening up new terrain for challenging philosophical, political, and economic questions regarding human-natural relations. Additionally, the management of these large and small devices and systems is increasingly done through the cloud, so that control over them is both very remote and removed from direct human or social control. The study of how to make technologies like artificial intelligence or the Internet of Things “explainable” has become its own area of research because it is so difficult to understand how they work or what is at fault when something goes wrong (Gunning and Aha 2019) .

This growing complexity makes it more difficult than ever—and more imperative than ever—for scholars to probe how technological advancements are altering life around the world in both positive and negative ways and what social, political, and legal tools are needed to help shape the development and design of technology in beneficial directions. This can seem like an impossible task in light of the rapid pace of technological change and the sense that its continued advancement is inevitable, but many countries around the world are only just beginning to take significant steps toward regulating computer technologies and are still in the process of radically rethinking the rules governing global data flows and exchange of technology across borders.

These are exciting times not just for technological development but also for technology policy—our technologies may be more advanced and complicated than ever but so, too, are our understandings of how they can best be leveraged, protected, and even constrained. The structures of technological systems as determined largely by government and institutional policies and those structures have tremendous implications for social organization and agency, ranging from open source, open systems that are highly distributed and decentralized, to those that are tightly controlled and closed, structured according to stricter and more hierarchical models. And just as our understanding of the governance of technology is developing in new and interesting ways, so, too, is our understanding of the social, cultural, environmental, and political dimensions of emerging technologies. We are realizing both the challenges and the importance of mapping out the full range of ways that technology is changing our society, what we want those changes to look like, and what tools we have to try to influence and guide those shifts.

Technology can be a source of tremendous optimism. It can help overcome some of the greatest challenges our society faces, including climate change, famine, and disease. For those who believe in the power of innovation and the promise of creative destruction to advance economic development and lead to better quality of life, technology is a vital economic driver (Schumpeter 1942) . But it can also be a tool of tremendous fear and oppression, embedding biases in automated decision-making processes and information-processing algorithms, exacerbating economic and social inequalities within and between countries to a staggering degree, or creating new weapons and avenues for attack unlike any we have had to face in the past. Scholars have even contended that the emergence of the term technology in the nineteenth and twentieth centuries marked a shift from viewing individual pieces of machinery as a means to achieving political and social progress to the more dangerous, or hazardous, view that larger-scale, more complex technological systems were a semiautonomous form of progress in and of themselves (Marx 2010) . More recently, technologists have sharply criticized what they view as a wave of new Luddites, people intent on slowing the development of technology and turning back the clock on innovation as a means of mitigating the societal impacts of technological change (Marlowe 1970) .

At the heart of fights over new technologies and their resulting global changes are often two conflicting visions of technology: a fundamentally optimistic one that believes humans use it as a tool to achieve greater goals, and a fundamentally pessimistic one that holds that technological systems have reached a point beyond our control. Technology philosophers have argued that neither of these views is wholly accurate and that a purely optimistic or pessimistic view of technology is insufficient to capture the nuances and complexity of our relationship to technology (Oberdiek and Tiles 1995) . Understanding technology and how we can make better decisions about designing, deploying, and refining it requires capturing that nuance and complexity through in-depth analysis of the impacts of different technological advancements and the ways they have played out in all their complicated and controversial messiness across the world.

These impacts are often unpredictable as technologies are adopted in new contexts and come to be used in ways that sometimes diverge significantly from the use cases envisioned by their designers. The internet, designed to help transmit information between computer networks, became a crucial vehicle for commerce, introducing unexpected avenues for crime and financial fraud. Social media platforms like Facebook and Twitter, designed to connect friends and families through sharing photographs and life updates, became focal points of election controversies and political influence. Cryptocurrencies, originally intended as a means of decentralized digital cash, have become a significant environmental hazard as more and more computing resources are devoted to mining these forms of virtual money. One of the crucial challenges in this area is therefore recognizing, documenting, and even anticipating some of these unexpected consequences and providing mechanisms to technologists for how to think through the impacts of their work, as well as possible other paths to different outcomes (Verbeek 2006) . And just as technological innovations can cause unexpected harm, they can also bring about extraordinary benefits—new vaccines and medicines to address global pandemics and save thousands of lives, new sources of energy that can drastically reduce emissions and help combat climate change, new modes of education that can reach people who would otherwise have no access to schooling. Regulating technology therefore requires a careful balance of mitigating risks without overly restricting potentially beneficial innovations.

Nations around the world have taken very different approaches to governing emerging technologies and have adopted a range of different technologies themselves in pursuit of more modern governance structures and processes (Braman 2009) . In Europe, the precautionary principle has guided much more anticipatory regulation aimed at addressing the risks presented by technologies even before they are fully realized. For instance, the European Union’s General Data Protection Regulation focuses on the responsibilities of data controllers and processors to provide individuals with access to their data and information about how that data is being used not just as a means of addressing existing security and privacy threats, such as data breaches, but also to protect against future developments and uses of that data for artificial intelligence and automated decision-making purposes. In Germany, Technische Überwachungsvereine, or TÜVs, perform regular tests and inspections of technological systems to assess and minimize risks over time, as the tech landscape evolves. In the United States, by contrast, there is much greater reliance on litigation and liability regimes to address safety and security failings after-the-fact. These different approaches reflect not just the different legal and regulatory mechanisms and philosophies of different nations but also the different ways those nations prioritize rapid development of the technology industry versus safety, security, and individual control. Typically, governance innovations move much more slowly than technological innovations, and regulations can lag years, or even decades, behind the technologies they aim to govern.

In addition to this varied set of national regulatory approaches, a variety of international and nongovernmental organizations also contribute to the process of developing standards, rules, and norms for new technologies, including the International Organization for Standardization­ and the International Telecommunication Union. These multilateral and NGO actors play an especially important role in trying to define appropriate boundaries for the use of new technologies by governments as instruments of control for the state.

At the same time that policymakers are under scrutiny both for their decisions about how to regulate technology as well as their decisions about how and when to adopt technologies like facial recognition themselves, technology firms and designers have also come under increasing criticism. Growing recognition that the design of technologies can have far-reaching social and political implications means that there is more pressure on technologists to take into consideration the consequences of their decisions early on in the design process (Vincenti 1993; Winner 1980) . The question of how technologists should incorporate these social dimensions into their design and development processes is an old one, and debate on these issues dates back to the 1970s, but it remains an urgent and often overlooked part of the puzzle because so many of the supposedly systematic mechanisms for assessing the impacts of new technologies in both the private and public sectors are primarily bureaucratic, symbolic processes rather than carrying any real weight or influence.

Technologists are often ill-equipped or unwilling to respond to the sorts of social problems that their creations have—often unwittingly—exacerbated, and instead point to governments and lawmakers to address those problems (Zuckerberg 2019) . But governments often have few incentives to engage in this area. This is because setting clear standards and rules for an ever-evolving technological landscape can be extremely challenging, because enforcement of those rules can be a significant undertaking requiring considerable expertise, and because the tech sector is a major source of jobs and revenue for many countries that may fear losing those benefits if they constrain companies too much. This indicates not just a need for clearer incentives and better policies for both private- and public-sector entities but also a need for new mechanisms whereby the technology development and design process can be influenced and assessed by people with a wider range of experiences and expertise. If we want technologies to be designed with an eye to their impacts, who is responsible for predicting, measuring, and mitigating those impacts throughout the design process? Involving policymakers in that process in a more meaningful way will also require training them to have the analytic and technical capacity to more fully engage with technologists and understand more fully the implications of their decisions.

At the same time that tech companies seem unwilling or unable to rein in their creations, many also fear they wield too much power, in some cases all but replacing governments and international organizations in their ability to make decisions that affect millions of people worldwide and control access to information, platforms, and audiences (Kilovaty 2020) . Regulators around the world have begun considering whether some of these companies have become so powerful that they violate the tenets of antitrust laws, but it can be difficult for governments to identify exactly what those violations are, especially in the context of an industry where the largest players often provide their customers with free services. And the platforms and services developed by tech companies are often wielded most powerfully and dangerously not directly by their private-sector creators and operators but instead by states themselves for widespread misinformation campaigns that serve political purposes (Nye 2018) .

Since the largest private entities in the tech sector operate in many countries, they are often better poised to implement global changes to the technological ecosystem than individual states or regulatory bodies, creating new challenges to existing governance structures and hierarchies. Just as it can be challenging to provide oversight for government use of technologies, so, too, oversight of the biggest tech companies, which have more resources, reach, and power than many nations, can prove to be a daunting task. The rise of network forms of organization and the growing gig economy have added to these challenges, making it even harder for regulators to fully address the breadth of these companies’ operations (Powell 1990) . The private-public partnerships that have emerged around energy, transportation, medical, and cyber technologies further complicate this picture, blurring the line between the public and private sectors and raising critical questions about the role of each in providing critical infrastructure, health care, and security. How can and should private tech companies operating in these different sectors be governed, and what types of influence do they exert over regulators? How feasible are different policy proposals aimed at technological innovation, and what potential unintended consequences might they have?

Conflict between countries has also spilled over significantly into the private sector in recent years, most notably in the case of tensions between the United States and China over which technologies developed in each country will be permitted by the other and which will be purchased by other customers, outside those two countries. Countries competing to develop the best technology is not a new phenomenon, but the current conflicts have major international ramifications and will influence the infrastructure that is installed and used around the world for years to come. Untangling the different factors that feed into these tussles as well as whom they benefit and whom they leave at a disadvantage is crucial for understanding how governments can most effectively foster technological innovation and invention domestically as well as the global consequences of those efforts. As much of the world is forced to choose between buying technology from the United States or from China, how should we understand the long-term impacts of those choices and the options available to people in countries without robust domestic tech industries? Does the global spread of technologies help fuel further innovation in countries with smaller tech markets, or does it reinforce the dominance of the states that are already most prominent in this sector? How can research universities maintain global collaborations and research communities in light of these national competitions, and what role does government research and development spending play in fostering innovation within its own borders and worldwide? How should intellectual property protections evolve to meet the demands of the technology industry, and how can those protections be enforced globally?

These conflicts between countries sometimes appear to challenge the feasibility of truly global technologies and networks that operate across all countries through standardized protocols and design features. Organizations like the International Organization for Standardization, the World Intellectual Property Organization, the United Nations Industrial Development Organization, and many others have tried to harmonize these policies and protocols across different countries for years, but have met with limited success when it comes to resolving the issues of greatest tension and disagreement among nations. For technology to operate in a global environment, there is a need for a much greater degree of coordination among countries and the development of common standards and norms, but governments continue to struggle to agree not just on those norms themselves but even the appropriate venue and processes for developing them. Without greater global cooperation, is it possible to maintain a global network like the internet or to promote the spread of new technologies around the world to address challenges of sustainability? What might help incentivize that cooperation moving forward, and what could new structures and process for governance of global technologies look like? Why has the tech industry’s self-regulation culture persisted? Do the same traditional drivers for public policy, such as politics of harmonization and path dependency in policy-making, still sufficiently explain policy outcomes in this space? As new technologies and their applications spread across the globe in uneven ways, how and when do they create forces of change from unexpected places?

These are some of the questions that we hope to address in the Technology and Global Change section through articles that tackle new dimensions of the global landscape of designing, developing, deploying, and assessing new technologies to address major challenges the world faces. Understanding these processes requires synthesizing knowledge from a range of different fields, including sociology, political science, economics, and history, as well as technical fields such as engineering, climate science, and computer science. A crucial part of understanding how technology has created global change and, in turn, how global changes have influenced the development of new technologies is understanding the technologies themselves in all their richness and complexity—how they work, the limits of what they can do, what they were designed to do, how they are actually used. Just as technologies themselves are becoming more complicated, so are their embeddings and relationships to the larger social, political, and legal contexts in which they exist. Scholars across all disciplines are encouraged to join us in untangling those complexities.

Josephine Wolff is an associate professor of cybersecurity policy at the Fletcher School of Law and Diplomacy at Tufts University. Her book You’ll See This Message When It Is Too Late: The Legal and Economic Aftermath of Cybersecurity Breaches was published by MIT Press in 2018.

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Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review

Stella timotheou.

1 CYENS Center of Excellence & Cyprus University of Technology (Cyprus Interaction Lab), Cyprus, CYENS Center of Excellence & Cyprus University of Technology, Nicosia-Limassol, Cyprus

Ourania Miliou

Yiannis dimitriadis.

2 Universidad de Valladolid (UVA), Spain, Valladolid, Spain

Sara Villagrá Sobrino

Nikoleta giannoutsou, romina cachia.

3 JRC - Joint Research Centre of the European Commission, Seville, Spain

Alejandra Martínez Monés

Andri ioannou, associated data.

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Digital technologies have brought changes to the nature and scope of education and led education systems worldwide to adopt strategies and policies for ICT integration. The latter brought about issues regarding the quality of teaching and learning with ICTs, especially concerning the understanding, adaptation, and design of the education systems in accordance with current technological trends. These issues were emphasized during the recent COVID-19 pandemic that accelerated the use of digital technologies in education, generating questions regarding digitalization in schools. Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses. Such results have engendered the need for schools to learn and build upon the experience to enhance their digital capacity and preparedness, increase their digitalization levels, and achieve a successful digital transformation. Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem, there is a need to show how these impacts are interconnected and identify the factors that can encourage an effective and efficient change in the school environments. For this purpose, we conducted a non-systematic literature review. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors that affect the schools’ digital capacity and digital transformation. The findings suggest that ICT integration in schools impacts more than just students’ performance; it affects several other school-related aspects and stakeholders, too. Furthermore, various factors affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the digital transformation process. The study results shed light on how ICTs can positively contribute to the digital transformation of schools and which factors should be considered for schools to achieve effective and efficient change.

Introduction

Digital technologies have brought changes to the nature and scope of education. Versatile and disruptive technological innovations, such as smart devices, the Internet of Things (IoT), artificial intelligence (AI), augmented reality (AR) and virtual reality (VR), blockchain, and software applications have opened up new opportunities for advancing teaching and learning (Gaol & Prasolova-Førland, 2021 ; OECD, 2021 ). Hence, in recent years, education systems worldwide have increased their investment in the integration of information and communication technology (ICT) (Fernández-Gutiérrez et al., 2020 ; Lawrence & Tar, 2018 ) and prioritized their educational agendas to adapt strategies or policies around ICT integration (European Commission, 2019 ). The latter brought about issues regarding the quality of teaching and learning with ICTs (Bates, 2015 ), especially concerning the understanding, adaptation, and design of education systems in accordance with current technological trends (Balyer & Öz, 2018 ). Studies have shown that despite the investment made in the integration of technology in schools, the results have not been promising, and the intended outcomes have not yet been achieved (Delgado et al., 2015 ; Lawrence & Tar, 2018 ). These issues were exacerbated during the COVID-19 pandemic, which forced teaching across education levels to move online (Daniel, 2020 ). Online teaching accelerated the use of digital technologies generating questions regarding the process, the nature, the extent, and the effectiveness of digitalization in schools (Cachia et al., 2021 ; König et al., 2020 ). Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses (Blaskó et al., 2021 ; Di Pietro et al, 2020 ). Such results have engendered the need for schools to learn and build upon the experience in order to enhance their digital capacity (European Commission, 2020 ) and increase their digitalization levels (Costa et al., 2021 ). Digitalization offers possibilities for fundamental improvement in schools (OECD, 2021 ; Rott & Marouane, 2018 ) and touches many aspects of a school’s development (Delcker & Ifenthaler, 2021 ) . However, it is a complex process that requires large-scale transformative changes beyond the technical aspects of technology and infrastructure (Pettersson, 2021 ). Namely, digitalization refers to “ a series of deep and coordinated culture, workforce, and technology shifts and operating models ” (Brooks & McCormack, 2020 , p. 3) that brings cultural, organizational, and operational change through the integration of digital technologies (JISC, 2020 ). A successful digital transformation requires that schools increase their digital capacity levels, establishing the necessary “ culture, policies, infrastructure as well as digital competence of students and staff to support the effective integration of technology in teaching and learning practices ” (Costa et al, 2021 , p.163).

Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem (Eng, 2005 ), there is a need to show how the different elements of the impact are interconnected and to identify the factors that can encourage an effective and efficient change in the school environment. To address the issues outlined above, we formulated the following research questions:

a) What is the impact of digital technologies on education?

b) Which factors might affect a school’s digital capacity and transformation?

In the present investigation, we conducted a non-systematic literature review of publications pertaining to the impact of digital technologies on education and the factors that affect a school’s digital capacity and transformation. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors which affect the schools’ digital capacity and digital transformation.

Methodology

The non-systematic literature review presented herein covers the main theories and research published over the past 17 years on the topic. It is based on meta-analyses and review papers found in scholarly, peer-reviewed content databases and other key studies and reports related to the concepts studied (e.g., digitalization, digital capacity) from professional and international bodies (e.g., the OECD). We searched the Scopus database, which indexes various online journals in the education sector with an international scope, to collect peer-reviewed academic papers. Furthermore, we used an all-inclusive Google Scholar search to include relevant key terms or to include studies found in the reference list of the peer-reviewed papers, and other key studies and reports related to the concepts studied by professional and international bodies. Lastly, we gathered sources from the Publications Office of the European Union ( https://op.europa.eu/en/home ); namely, documents that refer to policies related to digital transformation in education.

Regarding search terms, we first searched resources on the impact of digital technologies on education by performing the following search queries: “impact” OR “effects” AND “digital technologies” AND “education”, “impact” OR “effects” AND “ICT” AND “education”. We further refined our results by adding the terms “meta-analysis” and “review” or by adjusting the search options based on the features of each database to avoid collecting individual studies that would provide limited contributions to a particular domain. We relied on meta-analyses and review studies as these consider the findings of multiple studies to offer a more comprehensive view of the research in a given area (Schuele & Justice, 2006 ). Specifically, meta-analysis studies provided quantitative evidence based on statistically verifiable results regarding the impact of educational interventions that integrate digital technologies in school classrooms (Higgins et al., 2012 ; Tolani-Brown et al., 2011 ).

However, quantitative data does not offer explanations for the challenges or difficulties experienced during ICT integration in learning and teaching (Tolani-Brown et al., 2011 ). To fill this gap, we analyzed literature reviews and gathered in-depth qualitative evidence of the benefits and implications of technology integration in schools. In the analysis presented herein, we also included policy documents and reports from professional and international bodies and governmental reports, which offered useful explanations of the key concepts of this study and provided recent evidence on digital capacity and transformation in education along with policy recommendations. The inclusion and exclusion criteria that were considered in this study are presented in Table ​ Table1 1 .

Inclusion and exclusion criteria for the selection of resources on the impact of digital technologies on education

To ensure a reliable extraction of information from each study and assist the research synthesis we selected the study characteristics of interest (impact) and constructed coding forms. First, an overview of the synthesis was provided by the principal investigator who described the processes of coding, data entry, and data management. The coders followed the same set of instructions but worked independently. To ensure a common understanding of the process between coders, a sample of ten studies was tested. The results were compared, and the discrepancies were identified and resolved. Additionally, to ensure an efficient coding process, all coders participated in group meetings to discuss additions, deletions, and modifications (Stock, 1994 ). Due to the methodological diversity of the studied documents we began to synthesize the literature review findings based on similar study designs. Specifically, most of the meta-analysis studies were grouped in one category due to the quantitative nature of the measured impact. These studies tended to refer to student achievement (Hattie et al., 2014 ). Then, we organized the themes of the qualitative studies in several impact categories. Lastly, we synthesized both review and meta-analysis data across the categories. In order to establish a collective understanding of the concept of impact, we referred to a previous impact study by Balanskat ( 2009 ) which investigated the impact of technology in primary schools. In this context, the impact had a more specific ICT-related meaning and was described as “ a significant influence or effect of ICT on the measured or perceived quality of (parts of) education ” (Balanskat, 2009 , p. 9). In the study presented herein, the main impacts are in relation to learning and learners, teaching, and teachers, as well as other key stakeholders who are directly or indirectly connected to the school unit.

The study’s results identified multiple dimensions of the impact of digital technologies on students’ knowledge, skills, and attitudes; on equality, inclusion, and social integration; on teachers’ professional and teaching practices; and on other school-related aspects and stakeholders. The data analysis indicated various factors that might affect the schools’ digital capacity and transformation, such as digital competencies, the teachers’ personal characteristics and professional development, as well as the school’s leadership and management, administration, infrastructure, etc. The impacts and factors found in the literature review are presented below.

Impacts of digital technologies on students’ knowledge, skills, attitudes, and emotions

The impact of ICT use on students’ knowledge, skills, and attitudes has been investigated early in the literature. Eng ( 2005 ) found a small positive effect between ICT use and students' learning. Specifically, the author reported that access to computer-assisted instruction (CAI) programs in simulation or tutorial modes—used to supplement rather than substitute instruction – could enhance student learning. The author reported studies showing that teachers acknowledged the benefits of ICT on pupils with special educational needs; however, the impact of ICT on students' attainment was unclear. Balanskat et al. ( 2006 ) found a statistically significant positive association between ICT use and higher student achievement in primary and secondary education. The authors also reported improvements in the performance of low-achieving pupils. The use of ICT resulted in further positive gains for students, namely increased attention, engagement, motivation, communication and process skills, teamwork, and gains related to their behaviour towards learning. Evidence from qualitative studies showed that teachers, students, and parents recognized the positive impact of ICT on students' learning regardless of their competence level (strong/weak students). Punie et al. ( 2006 ) documented studies that showed positive results of ICT-based learning for supporting low-achieving pupils and young people with complex lives outside the education system. Liao et al. ( 2007 ) reported moderate positive effects of computer application instruction (CAI, computer simulations, and web-based learning) over traditional instruction on primary school student's achievement. Similarly, Tamim et al. ( 2011 ) reported small to moderate positive effects between the use of computer technology (CAI, ICT, simulations, computer-based instruction, digital and hypermedia) and student achievement in formal face-to-face classrooms compared to classrooms that did not use technology. Jewitt et al., ( 2011 ) found that the use of learning platforms (LPs) (virtual learning environments, management information systems, communication technologies, and information- and resource-sharing technologies) in schools allowed primary and secondary students to access a wider variety of quality learning resources, engage in independent and personalized learning, and conduct self- and peer-review; LPs also provide opportunities for teacher assessment and feedback. Similar findings were reported by Fu ( 2013 ), who documented a list of benefits and opportunities of ICT use. According to the author, the use of ICTs helps students access digital information and course content effectively and efficiently, supports student-centered and self-directed learning, as well as the development of a creative learning environment where more opportunities for critical thinking skills are offered, and promotes collaborative learning in a distance-learning environment. Higgins et al. ( 2012 ) found consistent but small positive associations between the use of technology and learning outcomes of school-age learners (5–18-year-olds) in studies linking the provision and use of technology with attainment. Additionally, Chauhan ( 2017 ) reported a medium positive effect of technology on the learning effectiveness of primary school students compared to students who followed traditional learning instruction.

The rise of mobile technologies and hardware devices instigated investigations into their impact on teaching and learning. Sung et al. ( 2016 ) reported a moderate effect on students' performance from the use of mobile devices in the classroom compared to the use of desktop computers or the non-use of mobile devices. Schmid et al. ( 2014 ) reported medium–low to low positive effects of technology integration (e.g., CAI, ICTs) in the classroom on students' achievement and attitude compared to not using technology or using technology to varying degrees. Tamim et al. ( 2015 ) found a low statistically significant effect of the use of tablets and other smart devices in educational contexts on students' achievement outcomes. The authors suggested that tablets offered additional advantages to students; namely, they reported improvements in students’ notetaking, organizational and communication skills, and creativity. Zheng et al. ( 2016 ) reported a small positive effect of one-to-one laptop programs on students’ academic achievement across subject areas. Additional reported benefits included student-centered, individualized, and project-based learning enhanced learner engagement and enthusiasm. Additionally, the authors found that students using one-to-one laptop programs tended to use technology more frequently than in non-laptop classrooms, and as a result, they developed a range of skills (e.g., information skills, media skills, technology skills, organizational skills). Haßler et al. ( 2016 ) found that most interventions that included the use of tablets across the curriculum reported positive learning outcomes. However, from 23 studies, five reported no differences, and two reported a negative effect on students' learning outcomes. Similar results were indicated by Kalati and Kim ( 2022 ) who investigated the effect of touchscreen technologies on young students’ learning. Specifically, from 53 studies, 34 advocated positive effects of touchscreen devices on children’s learning, 17 obtained mixed findings and two studies reported negative effects.

More recently, approaches that refer to the impact of gamification with the use of digital technologies on teaching and learning were also explored. A review by Pan et al. ( 2022 ) that examined the role of learning games in fostering mathematics education in K-12 settings, reported that gameplay improved students’ performance. Integration of digital games in teaching was also found as a promising pedagogical practice in STEM education that could lead to increased learning gains (Martinez et al., 2022 ; Wang et al., 2022 ). However, although Talan et al. ( 2020 ) reported a medium effect of the use of educational games (both digital and non-digital) on academic achievement, the effect of non-digital games was higher.

Over the last two years, the effects of more advanced technologies on teaching and learning were also investigated. Garzón and Acevedo ( 2019 ) found that AR applications had a medium effect on students' learning outcomes compared to traditional lectures. Similarly, Garzón et al. ( 2020 ) showed that AR had a medium impact on students' learning gains. VR applications integrated into various subjects were also found to have a moderate effect on students’ learning compared to control conditions (traditional classes, e.g., lectures, textbooks, and multimedia use, e.g., images, videos, animation, CAI) (Chen et al., 2022b ). Villena-Taranilla et al. ( 2022 ) noted the moderate effect of VR technologies on students’ learning when these were applied in STEM disciplines. In the same meta-analysis, Villena-Taranilla et al. ( 2022 ) highlighted the role of immersive VR, since its effect on students’ learning was greater (at a high level) across educational levels (K-6) compared to semi-immersive and non-immersive integrations. In another meta-analysis study, the effect size of the immersive VR was small and significantly differentiated across educational levels (Coban et al., 2022 ). The impact of AI on education was investigated by Su and Yang ( 2022 ) and Su et al. ( 2022 ), who showed that this technology significantly improved students’ understanding of AI computer science and machine learning concepts.

It is worth noting that the vast majority of studies referred to learning gains in specific subjects. Specifically, several studies examined the impact of digital technologies on students’ literacy skills and reported positive effects on language learning (Balanskat et al., 2006 ; Grgurović et al., 2013 ; Friedel et al., 2013 ; Zheng et al., 2016 ; Chen et al., 2022b ; Savva et al., 2022 ). Also, several studies documented positive effects on specific language learning areas, namely foreign language learning (Kao, 2014 ), writing (Higgins et al., 2012 ; Wen & Walters, 2022 ; Zheng et al., 2016 ), as well as reading and comprehension (Cheung & Slavin, 2011 ; Liao et al., 2007 ; Schwabe et al., 2022 ). ICTs were also found to have a positive impact on students' performance in STEM (science, technology, engineering, and mathematics) disciplines (Arztmann et al., 2022 ; Bado, 2022 ; Villena-Taranilla et al., 2022 ; Wang et al., 2022 ). Specifically, a number of studies reported positive impacts on students’ achievement in mathematics (Balanskat et al., 2006 ; Hillmayr et al., 2020 ; Li & Ma, 2010 ; Pan et al., 2022 ; Ran et al., 2022 ; Verschaffel et al., 2019 ; Zheng et al., 2016 ). Furthermore, studies documented positive effects of ICTs on science learning (Balanskat et al., 2006 ; Liao et al., 2007 ; Zheng et al., 2016 ; Hillmayr et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ; Lei et al., 2022a ). Çelik ( 2022 ) also noted that computer simulations can help students understand learning concepts related to science. Furthermore, some studies documented that the use of ICTs had a positive impact on students’ achievement in other subjects, such as geography, history, music, and arts (Chauhan, 2017 ; Condie & Munro, 2007 ), and design and technology (Balanskat et al., 2006 ).

More specific positive learning gains were reported in a number of skills, e.g., problem-solving skills and pattern exploration skills (Higgins et al., 2012 ), metacognitive learning outcomes (Verschaffel et al., 2019 ), literacy skills, computational thinking skills, emotion control skills, and collaborative inquiry skills (Lu et al., 2022 ; Su & Yang, 2022 ; Su et al., 2022 ). Additionally, several investigations have reported benefits from the use of ICT on students’ creativity (Fielding & Murcia, 2022 ; Liu et al., 2022 ; Quah & Ng, 2022 ). Lastly, digital technologies were also found to be beneficial for enhancing students’ lifelong learning skills (Haleem et al., 2022 ).

Apart from gaining knowledge and skills, studies also reported improvement in motivation and interest in mathematics (Higgins et. al., 2019 ; Fadda et al., 2022 ) and increased positive achievement emotions towards several subjects during interventions using educational games (Lei et al., 2022a ). Chen et al. ( 2022a ) also reported a small but positive effect of digital health approaches in bullying and cyberbullying interventions with K-12 students, demonstrating that technology-based approaches can help reduce bullying and related consequences by providing emotional support, empowerment, and change of attitude. In their meta-review study, Su et al. ( 2022 ) also documented that AI technologies effectively strengthened students’ attitudes towards learning. In another meta-analysis, Arztmann et al. ( 2022 ) reported positive effects of digital games on motivation and behaviour towards STEM subjects.

Impacts of digital technologies on equality, inclusion and social integration

Although most of the reviewed studies focused on the impact of ICTs on students’ knowledge, skills, and attitudes, reports were also made on other aspects in the school context, such as equality, inclusion, and social integration. Condie and Munro ( 2007 ) documented research interventions investigating how ICT can support pupils with additional or special educational needs. While those interventions were relatively small scale and mostly based on qualitative data, their findings indicated that the use of ICTs enabled the development of communication, participation, and self-esteem. A recent meta-analysis (Baragash et al., 2022 ) with 119 participants with different disabilities, reported a significant overall effect size of AR on their functional skills acquisition. Koh’s meta-analysis ( 2022 ) also revealed that students with intellectual and developmental disabilities improved their competence and performance when they used digital games in the lessons.

Istenic Starcic and Bagon ( 2014 ) found that the role of ICT in inclusion and the design of pedagogical and technological interventions was not sufficiently explored in educational interventions with people with special needs; however, some benefits of ICT use were found in students’ social integration. The issue of gender and technology use was mentioned in a small number of studies. Zheng et al. ( 2016 ) reported a statistically significant positive interaction between one-to-one laptop programs and gender. Specifically, the results showed that girls and boys alike benefitted from the laptop program, but the effect on girls’ achievement was smaller than that on boys’. Along the same lines, Arztmann et al. ( 2022 ) reported no difference in the impact of game-based learning between boys and girls, arguing that boys and girls equally benefited from game-based interventions in STEM domains. However, results from a systematic review by Cussó-Calabuig et al. ( 2018 ) found limited and low-quality evidence on the effects of intensive use of computers on gender differences in computer anxiety, self-efficacy, and self-confidence. Based on their view, intensive use of computers can reduce gender differences in some areas and not in others, depending on contextual and implementation factors.

Impacts of digital technologies on teachers’ professional and teaching practices

Various research studies have explored the impact of ICT on teachers’ instructional practices and student assessment. Friedel et al. ( 2013 ) found that the use of mobile devices by students enabled teachers to successfully deliver content (e.g., mobile serious games), provide scaffolding, and facilitate synchronous collaborative learning. The integration of digital games in teaching and learning activities also gave teachers the opportunity to study and apply various pedagogical practices (Bado, 2022 ). Specifically, Bado ( 2022 ) found that teachers who implemented instructional activities in three stages (pre-game, game, and post-game) maximized students’ learning outcomes and engagement. For instance, during the pre-game stage, teachers focused on lectures and gameplay training, at the game stage teachers provided scaffolding on content, addressed technical issues, and managed the classroom activities. During the post-game stage, teachers organized activities for debriefing to ensure that the gameplay had indeed enhanced students’ learning outcomes.

Furthermore, ICT can increase efficiency in lesson planning and preparation by offering possibilities for a more collaborative approach among teachers. The sharing of curriculum plans and the analysis of students’ data led to clearer target settings and improvements in reporting to parents (Balanskat et al., 2006 ).

Additionally, the use and application of digital technologies in teaching and learning were found to enhance teachers’ digital competence. Balanskat et al. ( 2006 ) documented studies that revealed that the use of digital technologies in education had a positive effect on teachers’ basic ICT skills. The greatest impact was found on teachers with enough experience in integrating ICTs in their teaching and/or who had recently participated in development courses for the pedagogical use of technologies in teaching. Punie et al. ( 2006 ) reported that the provision of fully equipped multimedia portable computers and the development of online teacher communities had positive impacts on teachers’ confidence and competence in the use of ICTs.

Moreover, online assessment via ICTs benefits instruction. In particular, online assessments support the digitalization of students’ work and related logistics, allow teachers to gather immediate feedback and readjust to new objectives, and support the improvement of the technical quality of tests by providing more accurate results. Additionally, the capabilities of ICTs (e.g., interactive media, simulations) create new potential methods of testing specific skills, such as problem-solving and problem-processing skills, meta-cognitive skills, creativity and communication skills, and the ability to work productively in groups (Punie et al., 2006 ).

Impacts of digital technologies on other school-related aspects and stakeholders

There is evidence that the effective use of ICTs and the data transmission offered by broadband connections help improve administration (Balanskat et al., 2006 ). Specifically, ICTs have been found to provide better management systems to schools that have data gathering procedures in place. Condie and Munro ( 2007 ) reported impacts from the use of ICTs in schools in the following areas: attendance monitoring, assessment records, reporting to parents, financial management, creation of repositories for learning resources, and sharing of information amongst staff. Such data can be used strategically for self-evaluation and monitoring purposes which in turn can result in school improvements. Additionally, they reported that online access to other people with similar roles helped to reduce headteachers’ isolation by offering them opportunities to share insights into the use of ICT in learning and teaching and how it could be used to support school improvement. Furthermore, ICTs provided more efficient and successful examination management procedures, namely less time-consuming reporting processes compared to paper-based examinations and smooth communications between schools and examination authorities through electronic data exchange (Punie et al., 2006 ).

Zheng et al. ( 2016 ) reported that the use of ICTs improved home-school relationships. Additionally, Escueta et al. ( 2017 ) reported several ICT programs that had improved the flow of information from the school to parents. Particularly, they documented that the use of ICTs (learning management systems, emails, dedicated websites, mobile phones) allowed for personalized and customized information exchange between schools and parents, such as attendance records, upcoming class assignments, school events, and students’ grades, which generated positive results on students’ learning outcomes and attainment. Such information exchange between schools and families prompted parents to encourage their children to put more effort into their schoolwork.

The above findings suggest that the impact of ICT integration in schools goes beyond students’ performance in school subjects. Specifically, it affects a number of school-related aspects, such as equality and social integration, professional and teaching practices, and diverse stakeholders. In Table ​ Table2, 2 , we summarize the different impacts of digital technologies on school stakeholders based on the literature review, while in Table ​ Table3 3 we organized the tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript.

The impact of digital technologies on schools’ stakeholders based on the literature review

Tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript

Additionally, based on the results of the literature review, there are many types of digital technologies with different affordances (see, for example, studies on VR vs Immersive VR), which evolve over time (e.g. starting from CAIs in 2005 to Augmented and Virtual reality 2020). Furthermore, these technologies are linked to different pedagogies and policy initiatives, which are critical factors in the study of impact. Table ​ Table3 3 summarizes the different tools and practices that have been used to examine the impact of digital technologies on education since 2005 based on the review results.

Factors that affect the integration of digital technologies

Although the analysis of the literature review demonstrated different impacts of the use of digital technology on education, several authors highlighted the importance of various factors, besides the technology itself, that affect this impact. For example, Liao et al. ( 2007 ) suggested that future studies should carefully investigate which factors contribute to positive outcomes by clarifying the exact relationship between computer applications and learning. Additionally, Haßler et al., ( 2016 ) suggested that the neutral findings regarding the impact of tablets on students learning outcomes in some of the studies included in their review should encourage educators, school leaders, and school officials to further investigate the potential of such devices in teaching and learning. Several other researchers suggested that a number of variables play a significant role in the impact of ICTs on students’ learning that could be attributed to the school context, teaching practices and professional development, the curriculum, and learners’ characteristics (Underwood, 2009 ; Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Tang et al., 2022 ).

Digital competencies

One of the most common challenges reported in studies that utilized digital tools in the classroom was the lack of students’ skills on how to use them. Fu ( 2013 ) found that students’ lack of technical skills is a barrier to the effective use of ICT in the classroom. Tamim et al. ( 2015 ) reported that students faced challenges when using tablets and smart mobile devices, associated with the technical issues or expertise needed for their use and the distracting nature of the devices and highlighted the need for teachers’ professional development. Higgins et al. ( 2012 ) reported that skills training about the use of digital technologies is essential for learners to fully exploit the benefits of instruction.

Delgado et al. ( 2015 ), meanwhile, reported studies that showed a strong positive association between teachers’ computer skills and students’ use of computers. Teachers’ lack of ICT skills and familiarization with technologies can become a constraint to the effective use of technology in the classroom (Balanskat et al., 2006 ; Delgado et al., 2015 ).

It is worth noting that the way teachers are introduced to ICTs affects the impact of digital technologies on education. Previous studies have shown that teachers may avoid using digital technologies due to limited digital skills (Balanskat, 2006 ), or they prefer applying “safe” technologies, namely technologies that their own teachers used and with which they are familiar (Condie & Munro, 2007 ). In this regard, the provision of digital skills training and exposure to new digital tools might encourage teachers to apply various technologies in their lessons (Condie & Munro, 2007 ). Apart from digital competence, technical support in the school setting has also been shown to affect teachers’ use of technology in their classrooms (Delgado et al., 2015 ). Ferrari et al. ( 2011 ) found that while teachers’ use of ICT is high, 75% stated that they needed more institutional support and a shift in the mindset of educational actors to achieve more innovative teaching practices. The provision of support can reduce time and effort as well as cognitive constraints, which could cause limited ICT integration in the school lessons by teachers (Escueta et al., 2017 ).

Teachers’ personal characteristics, training approaches, and professional development

Teachers’ personal characteristics and professional development affect the impact of digital technologies on education. Specifically, Cheok and Wong ( 2015 ) found that teachers’ personal characteristics (e.g., anxiety, self-efficacy) are associated with their satisfaction and engagement with technology. Bingimlas ( 2009 ) reported that lack of confidence, resistance to change, and negative attitudes in using new technologies in teaching are significant determinants of teachers’ levels of engagement in ICT. The same author reported that the provision of technical support, motivation support (e.g., awards, sufficient time for planning), and training on how technologies can benefit teaching and learning can eliminate the above barriers to ICT integration. Archer et al. ( 2014 ) found that comfort levels in using technology are an important predictor of technology integration and argued that it is essential to provide teachers with appropriate training and ongoing support until they are comfortable with using ICTs in the classroom. Hillmayr et al. ( 2020 ) documented that training teachers on ICT had an important effecton students’ learning.

According to Balanskat et al. ( 2006 ), the impact of ICTs on students’ learning is highly dependent on the teachers’ capacity to efficiently exploit their application for pedagogical purposes. Results obtained from the Teaching and Learning International Survey (TALIS) (OECD, 2021 ) revealed that although schools are open to innovative practices and have the capacity to adopt them, only 39% of teachers in the European Union reported that they are well or very well prepared to use digital technologies for teaching. Li and Ma ( 2010 ) and Hardman ( 2019 ) showed that the positive effect of technology on students’ achievement depends on the pedagogical practices used by teachers. Schmid et al. ( 2014 ) reported that learning was best supported when students were engaged in active, meaningful activities with the use of technological tools that provided cognitive support. Tamim et al. ( 2015 ) compared two different pedagogical uses of tablets and found a significant moderate effect when the devices were used in a student-centered context and approach rather than within teacher-led environments. Similarly, Garzón and Acevedo ( 2019 ) and Garzón et al. ( 2020 ) reported that the positive results from the integration of AR applications could be attributed to the existence of different variables which could influence AR interventions (e.g., pedagogical approach, learning environment, and duration of the intervention). Additionally, Garzón et al. ( 2020 ) suggested that the pedagogical resources that teachers used to complement their lectures and the pedagogical approaches they applied were crucial to the effective integration of AR on students’ learning gains. Garzón and Acevedo ( 2019 ) also emphasized that the success of a technology-enhanced intervention is based on both the technology per se and its characteristics and on the pedagogical strategies teachers choose to implement. For instance, their results indicated that the collaborative learning approach had the highest impact on students’ learning gains among other approaches (e.g., inquiry-based learning, situated learning, or project-based learning). Ran et al. ( 2022 ) also found that the use of technology to design collaborative and communicative environments showed the largest moderator effects among the other approaches.

Hattie ( 2008 ) reported that the effective use of computers is associated with training teachers in using computers as a teaching and learning tool. Zheng et al. ( 2016 ) noted that in addition to the strategies teachers adopt in teaching, ongoing professional development is also vital in ensuring the success of technology implementation programs. Sung et al. ( 2016 ) found that research on the use of mobile devices to support learning tends to report that the insufficient preparation of teachers is a major obstacle in implementing effective mobile learning programs in schools. Friedel et al. ( 2013 ) found that providing training and support to teachers increased the positive impact of the interventions on students’ learning gains. Trucano ( 2005 ) argued that positive impacts occur when digital technologies are used to enhance teachers’ existing pedagogical philosophies. Higgins et al. ( 2012 ) found that the types of technologies used and how they are used could also affect students’ learning. The authors suggested that training and professional development of teachers that focuses on the effective pedagogical use of technology to support teaching and learning is an important component of successful instructional approaches (Higgins et al., 2012 ). Archer et al. ( 2014 ) found that studies that reported ICT interventions during which teachers received training and support had moderate positive effects on students’ learning outcomes, which were significantly higher than studies where little or no detail about training and support was mentioned. Fu ( 2013 ) reported that the lack of teachers’ knowledge and skills on the technical and instructional aspects of ICT use in the classroom, in-service training, pedagogy support, technical and financial support, as well as the lack of teachers’ motivation and encouragement to integrate ICT on their teaching were significant barriers to the integration of ICT in education.

School leadership and management

Management and leadership are important cornerstones in the digital transformation process (Pihir et al., 2018 ). Zheng et al. ( 2016 ) documented leadership among the factors positively affecting the successful implementation of technology integration in schools. Strong leadership, strategic planning, and systematic integration of digital technologies are prerequisites for the digital transformation of education systems (Ređep, 2021 ). Management and leadership play a significant role in formulating policies that are translated into practice and ensure that developments in ICT become embedded into the life of the school and in the experiences of staff and pupils (Condie & Munro, 2007 ). Policy support and leadership must include the provision of an overall vision for the use of digital technologies in education, guidance for students and parents, logistical support, as well as teacher training (Conrads et al., 2017 ). Unless there is a commitment throughout the school, with accountability for progress at key points, it is unlikely for ICT integration to be sustained or become part of the culture (Condie & Munro, 2007 ). To achieve this, principals need to adopt and promote a whole-institution strategy and build a strong mutual support system that enables the school’s technological maturity (European Commission, 2019 ). In this context, school culture plays an essential role in shaping the mindsets and beliefs of school actors towards successful technology integration. Condie and Munro ( 2007 ) emphasized the importance of the principal’s enthusiasm and work as a source of inspiration for the school staff and the students to cultivate a culture of innovation and establish sustainable digital change. Specifically, school leaders need to create conditions in which the school staff is empowered to experiment and take risks with technology (Elkordy & Lovinelli, 2020 ).

In order for leaders to achieve the above, it is important to develop capacities for learning and leading, advocating professional learning, and creating support systems and structures (European Commission, 2019 ). Digital technology integration in education systems can be challenging and leadership needs guidance to achieve it. Such guidance can be introduced through the adoption of new methods and techniques in strategic planning for the integration of digital technologies (Ređep, 2021 ). Even though the role of leaders is vital, the relevant training offered to them has so far been inadequate. Specifically, only a third of the education systems in Europe have put in place national strategies that explicitly refer to the training of school principals (European Commission, 2019 , p. 16).

Connectivity, infrastructure, and government and other support

The effective integration of digital technologies across levels of education presupposes the development of infrastructure, the provision of digital content, and the selection of proper resources (Voogt et al., 2013 ). Particularly, a high-quality broadband connection in the school increases the quality and quantity of educational activities. There is evidence that ICT increases and formalizes cooperative planning between teachers and cooperation with managers, which in turn has a positive impact on teaching practices (Balanskat et al., 2006 ). Additionally, ICT resources, including software and hardware, increase the likelihood of teachers integrating technology into the curriculum to enhance their teaching practices (Delgado et al., 2015 ). For example, Zheng et al. ( 2016 ) found that the use of one-on-one laptop programs resulted in positive changes in teaching and learning, which would not have been accomplished without the infrastructure and technical support provided to teachers. Delgado et al. ( 2015 ) reported that limited access to technology (insufficient computers, peripherals, and software) and lack of technical support are important barriers to ICT integration. Access to infrastructure refers not only to the availability of technology in a school but also to the provision of a proper amount and the right types of technology in locations where teachers and students can use them. Effective technical support is a central element of the whole-school strategy for ICT (Underwood, 2009 ). Bingimlas ( 2009 ) reported that lack of technical support in the classroom and whole-school resources (e.g., failing to connect to the Internet, printers not printing, malfunctioning computers, and working on old computers) are significant barriers that discourage the use of ICT by teachers. Moreover, poor quality and inadequate hardware maintenance, and unsuitable educational software may discourage teachers from using ICTs (Balanskat et al., 2006 ; Bingimlas, 2009 ).

Government support can also impact the integration of ICTs in teaching. Specifically, Balanskat et al. ( 2006 ) reported that government interventions and training programs increased teachers’ enthusiasm and positive attitudes towards ICT and led to the routine use of embedded ICT.

Lastly, another important factor affecting digital transformation is the development and quality assurance of digital learning resources. Such resources can be support textbooks and related materials or resources that focus on specific subjects or parts of the curriculum. Policies on the provision of digital learning resources are essential for schools and can be achieved through various actions. For example, some countries are financing web portals that become repositories, enabling teachers to share resources or create their own. Additionally, they may offer e-learning opportunities or other services linked to digital education. In other cases, specific agencies of projects have also been set up to develop digital resources (Eurydice, 2019 ).

Administration and digital data management

The digital transformation of schools involves organizational improvements at the level of internal workflows, communication between the different stakeholders, and potential for collaboration. Vuorikari et al. ( 2020 ) presented evidence that digital technologies supported the automation of administrative practices in schools and reduced the administration’s workload. There is evidence that digital data affects the production of knowledge about schools and has the power to transform how schooling takes place. Specifically, Sellar ( 2015 ) reported that data infrastructure in education is developing due to the demand for “ information about student outcomes, teacher quality, school performance, and adult skills, associated with policy efforts to increase human capital and productivity practices ” (p. 771). In this regard, practices, such as datafication which refers to the “ translation of information about all kinds of things and processes into quantified formats” have become essential for decision-making based on accountability reports about the school’s quality. The data could be turned into deep insights about education or training incorporating ICTs. For example, measuring students’ online engagement with the learning material and drawing meaningful conclusions can allow teachers to improve their educational interventions (Vuorikari et al., 2020 ).

Students’ socioeconomic background and family support

Research show that the active engagement of parents in the school and their support for the school’s work can make a difference to their children’s attitudes towards learning and, as a result, their achievement (Hattie, 2008 ). In recent years, digital technologies have been used for more effective communication between school and family (Escueta et al., 2017 ). The European Commission ( 2020 ) presented data from a Eurostat survey regarding the use of computers by students during the pandemic. The data showed that younger pupils needed additional support and guidance from parents and the challenges were greater for families in which parents had lower levels of education and little to no digital skills.

In this regard, the socio-economic background of the learners and their socio-cultural environment also affect educational achievements (Punie et al., 2006 ). Trucano documented that the use of computers at home positively influenced students’ confidence and resulted in more frequent use at school, compared to students who had no home access (Trucano, 2005 ). In this sense, the socio-economic background affects the access to computers at home (OECD, 2015 ) which in turn influences the experience of ICT, an important factor for school achievement (Punie et al., 2006 ; Underwood, 2009 ). Furthermore, parents from different socio-economic backgrounds may have different abilities and availability to support their children in their learning process (Di Pietro et al., 2020 ).

Schools’ socioeconomic context and emergency situations

The socio-economic context of the school is closely related to a school’s digital transformation. For example, schools in disadvantaged, rural, or deprived areas are likely to lack the digital capacity and infrastructure required to adapt to the use of digital technologies during emergency periods, such as the COVID-19 pandemic (Di Pietro et al., 2020 ). Data collected from school principals confirmed that in several countries, there is a rural/urban divide in connectivity (OECD, 2015 ).

Emergency periods also affect the digitalization of schools. The COVID-19 pandemic led to the closure of schools and forced them to seek appropriate and connective ways to keep working on the curriculum (Di Pietro et al., 2020 ). The sudden large-scale shift to distance and online teaching and learning also presented challenges around quality and equity in education, such as the risk of increased inequalities in learning, digital, and social, as well as teachers facing difficulties coping with this demanding situation (European Commission, 2020 ).

Looking at the findings of the above studies, we can conclude that the impact of digital technologies on education is influenced by various actors and touches many aspects of the school ecosystem. Figure  1 summarizes the factors affecting the digital technologies’ impact on school stakeholders based on the findings from the literature review.

Factors that affect the impact of ICTs on education

The findings revealed that the use of digital technologies in education affects a variety of actors within a school’s ecosystem. First, we observed that as technologies evolve, so does the interest of the research community to apply them to school settings. Figure  2 summarizes the trends identified in current research around the impact of digital technologies on schools’ digital capacity and transformation as found in the present study. Starting as early as 2005, when computers, simulations, and interactive boards were the most commonly applied tools in school interventions (e.g., Eng, 2005 ; Liao et al., 2007 ; Moran et al., 2008 ; Tamim et al., 2011 ), moving towards the use of learning platforms (Jewitt et al., 2011 ), then to the use of mobile devices and digital games (e.g., Tamim et al., 2015 ; Sung et al., 2016 ; Talan et al., 2020 ), as well as e-books (e.g., Savva et al., 2022 ), to the more recent advanced technologies, such as AR and VR applications (e.g., Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ), or robotics and AI (e.g., Su & Yang, 2022 ; Su et al., 2022 ). As this evolution shows, digital technologies are a concept in flux with different affordances and characteristics. Additionally, from an instructional perspective, there has been a growing interest in different modes and models of content delivery such as online, blended, and hybrid modes (e.g., Cheok & Wong, 2015 ; Kazu & Yalçin, 2022 ; Ulum, 2022 ). This is an indication that the value of technologies to support teaching and learning as well as other school-related practices is increasingly recognized by the research and school community. The impact results from the literature review indicate that ICT integration on students’ learning outcomes has effects that are small (Coban et al., 2022 ; Eng, 2005 ; Higgins et al., 2012 ; Schmid et al., 2014 ; Tamim et al., 2015 ; Zheng et al., 2016 ) to moderate (Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Liao et al., 2007 ; Sung et al., 2016 ; Talan et al., 2020 ; Wen & Walters, 2022 ). That said, a number of recent studies have reported high effect sizes (e.g., Kazu & Yalçin, 2022 ).

Current work and trends in the study of the impact of digital technologies on schools’ digital capacity

Based on these findings, several authors have suggested that the impact of technology on education depends on several variables and not on the technology per se (Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Lei et al., 2022a ). While the impact of ICTs on student achievement has been thoroughly investigated by researchers, other aspects related to school life that are also affected by ICTs, such as equality, inclusion, and social integration have received less attention. Further analysis of the literature review has revealed a greater investment in ICT interventions to support learning and teaching in the core subjects of literacy and STEM disciplines, especially mathematics, and science. These were the most common subjects studied in the reviewed papers often drawing on national testing results, while studies that investigated other subject areas, such as social studies, were limited (Chauhan, 2017 ; Condie & Munro, 2007 ). As such, research is still lacking impact studies that focus on the effects of ICTs on a range of curriculum subjects.

The qualitative research provided additional information about the impact of digital technologies on education, documenting positive effects and giving more details about implications, recommendations, and future research directions. Specifically, the findings regarding the role of ICTs in supporting learning highlight the importance of teachers’ instructional practice and the learning context in the use of technologies and consequently their impact on instruction (Çelik, 2022 ; Schmid et al., 2014 ; Tamim et al., 2015 ). The review also provided useful insights regarding the various factors that affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the transformation process. Specifically, these factors include a) digital competencies; b) teachers’ personal characteristics and professional development; c) school leadership and management; d) connectivity, infrastructure, and government support; e) administration and data management practices; f) students’ socio-economic background and family support and g) the socioeconomic context of the school and emergency situations. It is worth noting that we observed factors that affect the integration of ICTs in education but may also be affected by it. For example, the frequent use of ICTs and the use of laptops by students for instructional purposes positively affect the development of digital competencies (Zheng et al., 2016 ) and at the same time, the digital competencies affect the use of ICTs (Fu, 2013 ; Higgins et al., 2012 ). As a result, the impact of digital technologies should be explored more as an enabler of desirable and new practices and not merely as a catalyst that improves the output of the education process i.e. namely student attainment.

Conclusions

Digital technologies offer immense potential for fundamental improvement in schools. However, investment in ICT infrastructure and professional development to improve school education are yet to provide fruitful results. Digital transformation is a complex process that requires large-scale transformative changes that presuppose digital capacity and preparedness. To achieve such changes, all actors within the school’s ecosystem need to share a common vision regarding the integration of ICTs in education and work towards achieving this goal. Our literature review, which synthesized quantitative and qualitative data from a list of meta-analyses and review studies, provided useful insights into the impact of ICTs on different school stakeholders and showed that the impact of digital technologies touches upon many different aspects of school life, which are often overlooked when the focus is on student achievement as the final output of education. Furthermore, the concept of digital technologies is a concept in flux as technologies are not only different among them calling for different uses in the educational practice but they also change through time. Additionally, we opened a forum for discussion regarding the factors that affect a school’s digital capacity and transformation. We hope that our study will inform policy, practice, and research and result in a paradigm shift towards more holistic approaches in impact and assessment studies.

Study limitations and future directions

We presented a review of the study of digital technologies' impact on education and factors influencing schools’ digital capacity and transformation. The study results were based on a non-systematic literature review grounded on the acquisition of documentation in specific databases. Future studies should investigate more databases to corroborate and enhance our results. Moreover, search queries could be enhanced with key terms that could provide additional insights about the integration of ICTs in education, such as “policies and strategies for ICT integration in education”. Also, the study drew information from meta-analyses and literature reviews to acquire evidence about the effects of ICT integration in schools. Such evidence was mostly based on the general conclusions of the studies. It is worth mentioning that, we located individual studies which showed different, such as negative or neutral results. Thus, further insights are needed about the impact of ICTs on education and the factors influencing the impact. Furthermore, the nature of the studies included in meta-analyses and reviews is different as they are based on different research methodologies and data gathering processes. For instance, in a meta-analysis, the impact among the studies investigated is measured in a particular way, depending on policy or research targets (e.g., results from national examinations, pre-/post-tests). Meanwhile, in literature reviews, qualitative studies offer additional insights and detail based on self-reports and research opinions on several different aspects and stakeholders who could affect and be affected by ICT integration. As a result, it was challenging to draw causal relationships between so many interrelating variables.

Despite the challenges mentioned above, this study envisaged examining school units as ecosystems that consist of several actors by bringing together several variables from different research epistemologies to provide an understanding of the integration of ICTs. However, the use of other tools and methodologies and models for evaluation of the impact of digital technologies on education could give more detailed data and more accurate results. For instance, self-reflection tools, like SELFIE—developed on the DigCompOrg framework- (Kampylis et al., 2015 ; Bocconi & Lightfoot, 2021 ) can help capture a school’s digital capacity and better assess the impact of ICTs on education. Furthermore, the development of a theory of change could be a good approach for documenting the impact of digital technologies on education. Specifically, theories of change are models used for the evaluation of interventions and their impact; they are developed to describe how interventions will work and give the desired outcomes (Mayne, 2015 ). Theory of change as a methodological approach has also been used by researchers to develop models for evaluation in the field of education (e.g., Aromatario et al., 2019 ; Chapman & Sammons, 2013 ; De Silva et al., 2014 ).

We also propose that future studies aim at similar investigations by applying more holistic approaches for impact assessment that can provide in-depth data about the impact of digital technologies on education. For instance, future studies could focus on different research questions about the technologies that are used during the interventions or the way the implementation takes place (e.g., What methodologies are used for documenting impact? How are experimental studies implemented? How can teachers be taken into account and trained on the technology and its functions? What are the elements of an appropriate and successful implementation? How is the whole intervention designed? On which learning theories is the technology implementation based?).

Future research could also focus on assessing the impact of digital technologies on various other subjects since there is a scarcity of research related to particular subjects, such as geography, history, arts, music, and design and technology. More research should also be done about the impact of ICTs on skills, emotions, and attitudes, and on equality, inclusion, social interaction, and special needs education. There is also a need for more research about the impact of ICTs on administration, management, digitalization, and home-school relationships. Additionally, although new forms of teaching and learning with the use of ICTs (e.g., blended, hybrid, and online learning) have initiated several investigations in mainstream classrooms, only a few studies have measured their impact on students’ learning. Additionally, our review did not document any study about the impact of flipped classrooms on K-12 education. Regarding teaching and learning approaches, it is worth noting that studies referred to STEM or STEAM did not investigate the impact of STEM/STEAM as an interdisciplinary approach to learning but only investigated the impact of ICTs on learning in each domain as a separate subject (science, technology, engineering, arts, mathematics). Hence, we propose future research to also investigate the impact of the STEM/STEAM approach on education. The impact of emerging technologies on education, such as AR, VR, robotics, and AI has also been investigated recently, but more work needs to be done.

Finally, we propose that future studies could focus on the way in which specific factors, e.g., infrastructure and government support, school leadership and management, students’ and teachers’ digital competencies, approaches teachers utilize in the teaching and learning (e.g., blended, online and hybrid learning, flipped classrooms, STEM/STEAM approach, project-based learning, inquiry-based learning), affect the impact of digital technologies on education. We hope that future studies will give detailed insights into the concept of schools’ digital transformation through further investigation of impacts and factors which influence digital capacity and transformation based on the results and the recommendations of the present study.

Acknowledgements

This project has received funding under Grant Agreement No Ref Ares (2021) 339036 7483039 as well as funding from the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy. The UVa co-authors would like also to acknowledge funding from the European Regional Development Fund and the National Research Agency of the Spanish Ministry of Science and Innovation, under project grant PID2020-112584RB-C32.

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Technology and Its Impact in the World Essay

Introduction, technology impacts, works cited.

Technology is defined as the use of tools, techniques and methods of organization in solving real world problems, which aims at performing specific tasks.

Technology has a profound root in the society; this is because today’s world relies on the advances in technology. These advances in technology in today’s world has sped people’s lives and made the world a smaller place to live in as it makes different locations closer to one another.

In addition, the fact that technology has become omnipresent in the world today due to its widespread use, is vital because it helps people in carrying out their chores in their daily livelihood. It is therefore important that the technology that exists be easily adaptable and able to solve the current world issues as human progress rate is increasing at an alarming rate (Oak 1).

The advances in technology have brought huge changes in the world today. Some of the areas where technology has brought important changes are as follows. First, technology has enabled the world in automating its critical processes in industries and households. The automobile industry has evolved from mechanical to automated automobiles simply because of the driving force that is technology.

Technology is applicable in performing tasks that are not accessible to man and are vital in automating crucial industrial processes. The technologies that are applicable when performing these crucial tasks include the use of robotics and artificial intelligence in carrying out challenging tasks such as space exploration and mining (Oak 1).

Another positive effect of technology is that it has changed the manner of communication. This has been made possible through the use computer technology; computers have the ability to process huge chunks of data at one go. Information digitization has proved to be a vital technology platform since it has made it possible in storing information and helps in enriching the information quality.

The advances in technology enable harnessing of water from natural sources to homes through robust transmission systems. Technology has brought the discovery of electricity that is important in lighting up the world. Electricity is easily generated by using renewable energy resources.

On the other hand, with all the advances in technology, it is unimaginable that technology has its side effects in the society even when the world is at the epitome of technology. In the medical technology world, technology can affect and also harm patients in cases where it involves a machine that has radiation rays.

On environmental technology, there is a lot of waste in terms of chemicals, which directly go back to the environment. Lastly, technology has a negative impact on people since they tend to be lazy and rely mostly on technology (Oak 1).

In conclusion, the advances brought about by technologies, which are the Internet, cell phones, and notebook computers are vital necessity for daily living. Due to these advances, it is easy for us to forget about those who suffer while attempting to provide for their basic needs, such as clean water, food and health care.

It is a good gesture by the developed world to make use of their technologies to help the underprivileged groups of people in the society. Through the continuous use of these technologies, there are advances that targets medical services, improved economy based on the Internet, emerging technologies in information systems sector, advanced farming methods and industrial sectors.

More importantly, educational needs for the people are taken into consideration by these technologies, since they help them become prosperous nations who do not require help from others but are able to get their own resources. Moreover, transferring technology from the developed world to the developing world has various benefits. There will be improvement in living standards, production efficiency and become a base for economic growth (Oak 1).

Oak, Manali. “ Positive Effects of Technology on Society .” Buzzle. 2011. Web.

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IvyPanda. (2023, October 31). Technology and Its Impact in the World. https://ivypanda.com/essays/impact-of-technology-in-the-world/

"Technology and Its Impact in the World." IvyPanda , 31 Oct. 2023, ivypanda.com/essays/impact-of-technology-in-the-world/.

IvyPanda . (2023) 'Technology and Its Impact in the World'. 31 October.

IvyPanda . 2023. "Technology and Its Impact in the World." October 31, 2023. https://ivypanda.com/essays/impact-of-technology-in-the-world/.

1. IvyPanda . "Technology and Its Impact in the World." October 31, 2023. https://ivypanda.com/essays/impact-of-technology-in-the-world/.

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IvyPanda . "Technology and Its Impact in the World." October 31, 2023. https://ivypanda.com/essays/impact-of-technology-in-the-world/.

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• Published: 22 April 2024

The influence of AI on the economic growth of different regions in China

• Shuang Lin 1 ,
• Minke Wang 2 ,
• Chongyi Jing 1 ,
• Shengda Zhang 1 ,
• Jiuhao Chen 1 &
• Rui Liu 3  

Scientific Reports volume  14 , Article number:  9169 ( 2024 ) Cite this article

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• Environmental economics
• Environmental social sciences

High-quality development plays a crucial role in China’s economic progress in the new era. It represents a new concept of advancement and mirrors the increasing aspirations of the populace for an improved standard of living. In this context, the role of artificial intelligence (AI) in promoting sustainable development cannot be overemphasized. This paper explores how AI technologies can drive the transition to a green, low-carbon and circular economy. We have established an index system to measure the development level of the artificial intelligence industry and the high-quality development of the economy, which is relevant to the current state of the artificial intelligence industry and the advancement of the economy. Panel data from 2008 to 2017 has been utilized for this purpose. Global principal component analysis method and entropy value method are employed in the evaluation. Through in-depth analysis of the application of artificial intelligence and environmental protection in various provinces and cities, we clarify that artificial intelligence promotes innovation, saves resources, and is conducive to the development of green economy in the new era.

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Introduction.

The world is facing unprecedented environmental challenges, from rising carbon emissions to the depletion of natural resources, which remind us that we must take action to change 1 , 2 . Adopting sustainable practices has become a priority for governments, businesses and individuals. At the intersection of technology and sustainability, AI has become a transformative force, offering unprecedented opportunities to advance a green, low-carbon and circular economy. In recent years, the rapid advancement and extensive utilization of artificial intelligence technology have led to the establishment of an industrial chain known as “artificial intelligence +”, giving rise to fresh formats, models, and directions for industrial development. Artificial intelligence serves as a crucial driving force behind a new wave of scientific and technological revolution and industrial transformation 3 , 4 , 5 . It can be seen that in the new round of information technology revolution, the artificial intelligence industry will become a new driving force for promoting high-quality economic development. At present, the new generation of artificial intelligence industry includes three levels: base layer, technology layer and application layer, and has a complete industrial chain. Its advantages are mainly reflected in: First, a number of technologies in the field of artificial intelligence industry technology layer are at the international frontier level; Second, the artificial intelligence industry occupies a huge market in the application layer field and has the support of information data. Promoting the development of artificial intelligence industry is an important way to accelerate innovation-driven development and industrial transformation and upgrading, and is a new driving force for reinforcing the advancement of superior economic growth 6 , 7 . At the same time, it is also the best choice to reduce environmental pollution and develop green and low-carbon economy 8 . At present, under the background of global economic development and the continuous improvement of international technological level, artificial intelligence technology and intelligent industry are entering a period of rapid development. Studies have pointed out that the scale of China's AI industry has reached 10.06 billion in 2018, with a growth rate of 43.3%, and will grow by 3.43 billion in 2022 9 , 10 . This indicates that there is significant potential for the development of China’s artificial intelligence industry. Currently, artificial intelligence technology and industry are rapidly integrating and developing, leading to the expansion of industrial chain layout, the formation of industrial chain clusters, and the promotion of transformation and upgrading in traditional industries as well as high-quality economic development. Based on this, China has incorporated artificial intelligence into the national strategic level, and attaches great importance to intelligent technological progress and industrial development 11 , 12 .

This paper uses a dynamic panel model to conduct an empirical study on how the artificial intelligence industry impacts the high-quality development of the green economy. The analysis takes into account the dynamic nature of economic growth and addresses endogenous problems caused by missing variables. This research provides a theoretical foundation for our country to promote high-quality economic development through the “artificial intelligence + X” initiative, thus holding certain theoretical value.

Literature review

Foreign scholars started the research on artificial intelligence and industrial development earlier, and the existing research literature mainly focuses on the connotation of artificial intelligence, the existing problems and risks of artificial intelligence development, application prospects, and technological innovation. First of all, there are different interpretations about the meaning of artificial intelligence, and no unified definition has been given. Minsky pointed out that artificial intelligence is a complex science that replaces human intelligence; Min also believes that AI is a “thinking machine” developed and created, which can accurately imitate, learn, and replace human intelligence 13 , 14 . Secondly, as the theoretical connotation of artificial intelligence continues to improve and expand, its technology has a significant impact on the development of various industries and overall economic operation. The influence of artificial intelligence on industrial development is examined through the lens of its technological applications. The research of Mian 15 shows that artificial intelligence can not only promote the exchange of information in the industrial supply chain, but also replace assets with information elements, reduce unnecessary transaction costs, and help enterprises improve production and operation efficiency. Based on the development theory of artificial intelligence, Frey and Osborne 15 took the study of the degree of response of intelligent machines to work as an entry point and found that the American labor market would change unpredictably due to intelligent technology.

In recent years, there has been a gradual increase in the development of domestic artificial intelligence and industry. Scholars have primarily focused on the national policy context for the development path of artificial intelligence, its application fields, and quantitative analysis. Firstly, considering the support of national policies, discussions have been made on the choice of social governance path in the era of artificial intelligence, as well as the impact of human intelligence technology on industrial transformation and employment. Mei 16 pointed out that in this era of intelligence, it is important to effectively utilize intelligent technology to enhance national governance efficiency and mitigate any negative effects on employment caused by artificial intelligence. Xie 17 pointed out that the scale of China’s AI industry is gradually expanding, the artificial intelligence service platform is becoming more and more perfect, and the industrial development layout of Shanghai, Guangzhou and Shenzhen is formed in the north.

Materials and methods

The research methods of this paper mainly include 19 , 20 :

The use of logical reasoning methods. In exploring the mechanism analysis of artificial intelligence industry on high-quality economic development, the method of logical reasoning is used to analyze the internal impact of human intelligence industry and high-quality economic development from the level of economic effect, environmental effect and social effect.

Using statistical techniques, this paper employs the global principal component analysis method and entropy value method to assess the development level of the artificial intelligence industry and the high-quality development level of the economy. The evaluation and instruction system is constructed to measure the artificial intelligence industry development index and economic high-quality development index of various provinces in China from 2008 to 2022. The region and time characteristics of the index variables are depicted using statistical software to create tables in multiple dimensions.

Using econometric methods. In the empirical part of this paper, the dynamic panel model is mainly constructed to explore the research and analysis of the influence of artificial intelligence industry on high-quality economic development. At the same time, under the premise of considering the endogenous problem, the panel fixed effect and panel random effect measurement methods are adopted, and the system generalized moment estimation method (GMM) is used for regression 18 , 19 , 20 , 21 . At the same time, when analyzing the data stationarity, the unit root stationarity test is used.

Artificial intelligence industry index selection and data sources. The data of the artificial intelligence industry development index and high-quality economic development index involved in the econometric model come from the statistical data and related research of the national statistical database, EPS database, and China Economic network database.

Artificial intelligence industry index measurement. Although the selection of artificial intelligence industry index construction considers the development of artificial intelligence industry from different aspects, there is still a correlation between various indicators. Therefore, in order to further reduce the correlation among indicators, this paper adopts the global principal component analysis method to combine basic support, integrated application, innovation ability and environmental protection into a comprehensive index, namely artificial intelligence industry index.

Artificial intelligence industry is a very broad field, it refers to the artificial intelligence technology as the core, by the foundation support and application integration of the industry. Basic support is mainly composed of information data and calculation ability support, which provides basic elements for the development of artificial intelligence technology and its industry; Integration specifically refers to various subsectors including intelligent finance, and robotics. From the industrial level, the artificial intelligence industry: one is the industry with artificial intelligence technology as the core. Such industries directly provide products and services through artificial intelligence technology itself, effectively improving the efficiency of industrial operations. The other is the artificial intelligence technology integration industry. This kind of industry is mainly through the innovative integration of artificial intelligence technology and traditional industries, so as to upgrade traditional industries and form new intelligent industries. These two types of artificial intelligence industries are based on artificial intelligence technology as the core element of development, with intelligent agglomeration, data-driven, information sharing and other characteristics.

Artificial intelligence industry index selection and data sources

The data used in this paper is primarily sourced from various Chinese statistical yearbooks covering the period from 2008 to 2022, including the China Statistical Yearbook, China High-tech Industry Statistical Yearbook, China Science and Technology Statistical Yearbook, and China Electronic Information Industry Statistical Yearbook.

Artificial intelligence industry index measurement

Although the selection of artificial intelligence industry index construction considers the development of artificial intelligence industry from different aspects, there is still a correlation between various indicators. Therefore, in order to further reduce the correlation between indicators, this paper adopts the global principal component analysis method to combine these 15 indicators into a comprehensive index, namely, artificial intelligence industry index. In this paper, the SPSS22 econometric and statistical software is used to conduct principal component analysis on the AI industry development indicators of 30 provincial cities in China from 2008 to 2022, and then the AI industry development index is constructed.

The test results of this paper show (Table 1 ) that the appropriate measure value of KMO sampling is 0.72, greater than 0.6, which meets the prerequisite of principal component analysis, indicating that the artificial intelligence industry index data is suitable for principal component analysis ( p  < 0.05). Determine the principal component factors. According to the theoretical experience of principal component analysis, it is more appropriate for the component with eigenvalue greater than 1 to be the principal component. When the eigenvalue is greater than 1, only the first two components are available, and the cumulative variance contribution rate is 82.03%, indicating that these two components can fully reflect the information of the original data. Therefore, we extract these two components as the main components and record them as F1 and F2 respectively.

Regression estimation method was used to calculate the factor score coefficient and the weight of each index, see Table 2 .

Determine the artificial intelligence industry index. According to the results of principal component analysis, the development index of artificial intelligence industry is determined. Artificial intelligence industry (AI) is used to represent the artificial intelligence industry, and the artificial intelligence industry index is expressed as 14 :

According to previous research, the development level of artificial intelligence industry in each province is standardized:

where Fi is the comprehensive factor score of i province. The maxFi and min Fi are the maximum and minimum values of comprehensive factor scores corresponding to province i.

Calculation results and analysis of artificial intelligence industry indicators

From the perspective of the overall change characteristics: from 2008 to 2017, with the comprehensive optimization of China’s artificial intelligence industry development policy, the steady improvement of innovation capacity, the continuous consolidation of the industrial foundation, the continuous improvement of the development environment, and the continuous deepening of integration and application, the development of artificial intelligence industry in 30 provinces in China showed a good trend of rising year by year. This is the result of the state’s strong support for the construction of intelligent industries, continuous promotion of artificial intelligence technology innovation and progress, and the rapid development of artificial intelligence industry in the whole society. Our research shows the artificial intelligence industry development index and its average national artificial intelligence industry during 2008–2017, and Fig.  1 shows the overall trend of the average national artificial intelligence industry development level during 2008–2017. China's artificial intelligence industry development index is 0.481, which is higher than the national average level of artificial intelligence industry development in 15 provinces, of which Beijing (1.033), Shanghai (0.946), Jiangsu (0.876), Guangdong (0.867) and Zhejiang (0.830) are ranked in the top 5 in China’s artificial intelligence industry development. Shanxi (0.189), Gansu (0.160), Xinjiang (0.159), Hainan (0.061), and Qinghai (0.052) are ranked in the bottom 5 of China, among which Hainan is divided into the eastern region, but its economic development and scientific research level are still a certain gap with other eastern regions, so the development level of artificial intelligence industry is low. This shows that the development trend of China's artificial intelligence industry is similar to that of economic development, and also shows a decreasing trend year by year from the southeast coast to the northwest inland.

National average artificial intelligence industry development level from 2008 to 2017.

From the perspective of regional fluctuation characteristics (as shown in Fig.  2 ): 1. The average development index of industrial intelligence industry is 0.713, exceeding the national average level of 0.232, and is in an absolute leading position. In the eastern region, Beijing and Shandong, relying on the construction of smart city clusters around the Bohai Sea economic Circle, have core competitive advantages in intelligent technology research and development and intelligent industry cultivation. Revised: Leveraging the manufacturing hub of the Yangtze River Delta Economic Belt, Shanghai, Jiangsu and Zhejiang have actively advanced intelligent manufacturing technologies such as the Internet of Things and cloud computing, driving widespread development of the artificial intelligence industry across various social and economic sectors. Building on the competitive edge of the Pearl River Delta Economic Circle and the Guangdong-Hong Kong-Macao Greater Bay Area, Guangdong will further enhance its strategic layout and construction in the artificial intelligence industry to achieve leading-edge development in this field. 2. Balanced development of the central region. The average development index of artificial intelligence industry in the central region is 0.462, and the development is balanced within the region. Among them, the development level of artificial intelligence industry in Anhui (0.587), Henan (0.523) and Hubei (0.563) is relatively close, and is in a higher position in the central region. 3 Polarization in the western region. The average index of artificial intelligence industry development in the western region is 0.309, lagging behind the national average level of 0.172, and the development in the region is significantly polarized. The combination of policies and industries in Sichuan and Chongqing has effectively promoted the rapid development of the artificial intelligence industry, and the artificial intelligence industry index ranks 9th and 16th in the country respectively. Shaanxi and Guizhou have outstanding advantages in science and technology research and development and big data application, accelerating artificial intelligence and industrial development. The development level of artificial intelligence industry in other western regions is relatively backward. 4. The level of northeast China is not high. The average development index of artificial intelligence industry in Northeast China is 0.378, higher than the level of western regions, but still lower than the national development level of 0.103. Among them, Liaoning’s artificial intelligence industry development index of 0.554 is higher than the national average, ranking first in Northeast China. The overall level of development of artificial intelligence industry in Northeast China is not high, which may be related to factors such as insufficient internal impetus for economic development in Northeast China, difficulty in converting old and new economic momentum, small scale of development of artificial intelligence industry, and insufficient investment.

Change trend of sub-regional artificial intelligence industry development index (From left to right, East, central, West, Northeast of China).

This paper analyzes the impact of the artificial intelligence industry on high-quality economic development by incorporating the artificial intelligence industry development index into the framework of analysis. In order to account for the dynamic nature of high-quality economic development, we also include a first-order lag variable of the explained variable in our analysis.

In the above formula, t and i respectively represent different years and provinces; HQDit = the high-quality economic development index of the i province in the t year; HQDit-1  = the high-quality economic development index of the i province in the one period lagging behind; AIit  = the artificial intelligence industry development index of the i province in the t year; Xit  = the control variable of the i province in the t year. B 0 represents the intercept term, Eit  = the random error term. B 1,2,3  = parameter to be estimated, B 2  = the influence coefficient of artificial intelligence industry on high-quality economic growth. Considering the economic meaning of regression coefficient, and in order to increase the smoothness of data, logarithmic processing is carried out on the variables of artificial intelligence industry index, economic high-quality development index and technological innovation.

The findings indicate that the artificial intelligence industry index, as the core explanatory variable, has a positive impact on high-quality economic development and passes the significance test at the 1% level. Economic development is a continuous process of gradual transformation.

At present, Chinese and foreign scholars have studied the relationship between artificial intelligence industry and economic growth, and scholars generally agree that artificial intelligence industry has a role in promoting economic growth. Although there is a wealth of existing literature on the influence of artificial intelligence on economic growth, there is a lack of systematic analysis in the literature regarding the impact of the artificial intelligence industry on the mechanism for high-quality economic development. This paper examines the economic, environmental, and social implications of the artificial intelligence industry on high-quality economic growth based on the concept of high quality 16 , 17 . After the introduction of China’s artificial intelligence industry development strategy, artificial intelligence has significantly contributed to enhancing the country's economic growth. Therefore, it is crucial to understand the impact of the artificial intelligence industry on high-quality economic development in order to further strengthen China’s overall artificial intelligence industry and promote high-quality economic growth driven by artificial intelligence. This study utilizes provincial panel data from 2008 to 2017 to construct a provincial index for artificial intelligence industry development and economic high-quality development, and empirically analyzes the influence of the artificial intelligence industry on high-quality economic development.

The role of artificial intelligence industry in promoting innovation ability

The artificial intelligence industry improves its innovation ability by accelerating the introduction of intelligent technologies. In terms of optimizing traditional elements, the development of artificial intelligence industry can not only introduce high-end information technology elements such as cloud computing, new Internet, and big data, expand people's learning ability, and accelerate human capital accumulation, but also build industrial operation systems, improve technological innovation systems, and improve resource allocation efficiency under the information environment of digital economy operation. In terms of promoting technological innovation, the artificial intelligence industry reduces the uncertainty caused by the introduction of innovative technology by accelerating the introduction of technology, which is conducive to reducing the risk of technological innovation investment and stimulating innovation efficiency 18 , 19 . At the same time, intelligent technology provides a new driving force for innovation efficiency, which is based on information flow, effectively integrates technology, capital, manpower and material, and deeply integrates with cloud computing, Internet and other industries, effectively improving management innovation, institutional innovation and model innovation. From the perspective of service innovation, knowledge and technology innovation and product innovation, collaborative innovation efficiency can be achieved.

This study suggests that the artificial intelligence sector has a substantial influence on the advancement of high-quality economy. However, it also indicates that China’s current level of development in this industry is not sufficiently high and there is still ample room for enhancement. The future establishment of the artificial intelligence industry can be initiated from the following perspectives. 1. It is important to enhance the development and funding of the artificial intelligence sector. There is a need to continuously diversify, broaden, and innovate the range of services offered by artificial intelligence and enhance the quality of intelligent services. Efforts should be made to expand the artificial intelligence industry and establish a cluster of intelligent industrial chains. Furthermore, increasing investment in advanced artificial intelligence technology and application production will contribute to the growth of this industry. 2. It is essential to establish policies that support the growth of the artificial intelligence industry within the framework of advancing the national economy. These policies should leverage the strengths of private capital and drive forward the high-quality development of artificial intelligence. 3. Rebalance the distribution of resources in the artificial intelligence industry. There is a noticeable disparity in resource allocation between the eastern, central, and western regions in the development of the artificial intelligence industry. It is important to enhance the competitive advantage of the artificial intelligence industry in the eastern coastal areas while also promoting its growth in the central and western regions. Efforts should be made to encourage the flow of artificial intelligence industry resources and national policies to these regions, thereby improving overall resource allocation levels within the industry.

The artificial intelligence industry improves its innovation ability by promoting the transformation of intelligent technologies. The artificial intelligence industry promotes the transformation of intellectual energy technology, which can well drive the development of a series of related industries, promote industrial division of labor, coordination and development, and thus form industrial innovation cooperation 22 , 23 . At the same time, the artificial intelligence industry has formed full innovation cooperation, making enterprises, government organizations, service agencies, social groups and other organizations jointly build an innovation network system with the artificial intelligence industry as the core. For example, Weibo, wechat and other social platforms can spread and share news quickly through a huge consumer group, so the greater the network value created, the more enterprises and consumers can be attracted to enter. Based on the transformation and application of intelligent technology, the artificial intelligence industry has improved its innovation ability and driven high-quality economic growth through cooperation between different industrial chains 24 , 25 .

Encourage the advancement of the strategy involving “artificial intelligence + X” and enhance the overall economic competitiveness of the area

This paper demonstrates that the artificial intelligence industry is an important driving force that cannot be ignored for high-quality economic growth, with direct and indirect impacts. To promote the development of the “artificial intelligence + X” strategy and improve the overall level of regional economy, we should pay attention to the following aspects 26 , 27 , 28 , 29 , 30 , 31 : 1. Strengthen the intelligent, intensive and collaborative characteristics of artificial intelligence, build a more efficient, convenient and flexible “artificial intelligence + X” regional intelligent innovation model, and maximize the competitive advantage of the artificial intelligence industry. 2. Develop a patent protection system for artificial intelligence technology. The lack of relevant laws and regulations on the protection of artificial intelligence technology patent achievements, the state should strengthen the protection of artificial intelligence industry patents, and create a good development environment for the development of artificial intelligence industry. 3. Build an intelligence sharing service platform to provide intelligent services. Through the construction of an intelligent sharing service platform integrating government, universities and enterprises, new knowledge, new technology, massive information, big data and other new production factors are shared on the intelligent service platform to form an intelligent sharing platform in characteristic fields and provide intelligent services in an all-round way, thus improving regional economic strength.

Promote differentiated intelligent technology support and balance the regional differences in the distribution of artificial intelligence industry resources

This study reveals that there is a clear imbalance in the spatial distribution of the artificial intelligence industry’s development level. This suggests that the “artificial intelligence +” strategy is not fixed and unchangeable, and it is important to implement dynamic and differentiated intelligent technology to address regional disparities in the distribution of artificial intelligence industry resources. Additionally, there are significant regional differences in the role of the artificial intelligence industry in promoting high-quality economic development. The eastern region, in particular, has higher requirements for the development 32 , 33 . The artificial intelligence industry in the central and western regions lags behind that in the eastern regions. It is important to boost the development of artificial intelligence industry in these areas, narrow the economic gap with developed regions, and achieve high-quality and balanced economic growth. Additionally, it is crucial for the government to consider the actual situation and empirical laws of artificial intelligence industry development when implementing industrial policies, as this will enhance the competitiveness of artificial intelligence technology in regional economic development and effectively promote high-quality economic growth through human intelligence.

Conclusions

The impact of the artificial intelligence industry on high-quality economic development is intricate. This paper examines the artificial intelligence industry within the framework of a high-quality economic development system, based on the concept of new development. By analyzing its positive effects on economic innovation, coordination, environmental sustainability, openness and sharing from economic, environmental and social perspectives, this paper aims to understand the mechanism effect of artificial intelligence industry on high-quality economic development. Therefore, enhancing the development of the artificial intelligence industrial system will contribute to advancing high-quality economic growth. Research on the impact of the artificial intelligence industry on high-quality economic development yields diverse results. Specifically, during the period of 2008–2012, when the economy was still in a phase of rapid growth, the influence of the artificial intelligence industry on high-quality economic development was not very apparent; however, from 2012 to 2017, as the economy transitioned into a stage of high-quality growth, it became evident that the artificial intelligence industry played a significant role in promoting such growth. Rephrased: The impact of the artificial intelligence industry on the high-quality development of the eastern, central, and western regions varies significantly based on regional distribution. This discrepancy is attributed to varying levels of high-quality economic development in these regions, with the rapid growth of the artificial intelligence industry in the eastern region having a more pronounced effect on high-quality economic development. Conversely, the impact of the artificial intelligence industry in the central and western regions on high-quality economic development is relatively weak.

Data availability

The experimental data used to support the findings of this study are available from the corresponding author on request.

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This research is funded by China Scholarship Council and the local innovation sub-project of the Western Project of the Sichuan Provincial (Grant No: 202108510117).

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School of Economics and Management, Civil Aviation Flight University of China, Deyang, 618307, China

Shuang Lin, Chongyi Jing, Shengda Zhang & Jiuhao Chen

School of Airport Engineering, Civil Aviation Flight University of China, Deyang, 618307, China

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Writing—original draft, S.L.; formal analysis, S.L.; Investigation, M.W. and C.J.; Methodology, M.W. and J.C.; Resources, R.L.; Writing—review and editing, S.Z. All authors have read and agreed to the published version of the manuscript.

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Lin, S., Wang, M., Jing, C. et al. The influence of AI on the economic growth of different regions in China. Sci Rep 14 , 9169 (2024). https://doi.org/10.1038/s41598-024-59968-7

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DOI : https://doi.org/10.1038/s41598-024-59968-7

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