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  • Published: 23 November 2017

Exploring the impact of artificial intelligence on teaching and learning in higher education

  • Stefan A. D. Popenici   ORCID: orcid.org/0000-0002-0323-2945 1 &
  • Sharon Kerr 2  

Research and Practice in Technology Enhanced Learning volume  12 , Article number:  22 ( 2017 ) Cite this article

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This paper explores the phenomena of the emergence of the use of artificial intelligence in teaching and learning in higher education. It investigates educational implications of emerging technologies on the way students learn and how institutions teach and evolve. Recent technological advancements and the increasing speed of adopting new technologies in higher education are explored in order to predict the future nature of higher education in a world where artificial intelligence is part of the fabric of our universities. We pinpoint some challenges for institutions of higher education and student learning in the adoption of these technologies for teaching, learning, student support, and administration and explore further directions for research.

Introduction

The future of higher education is intrinsically linked with developments on new technologies and computing capacities of the new intelligent machines. In this field, advances in artificial intelligence open to new possibilities and challenges for teaching and learning in higher education, with the potential to fundamentally change governance and the internal architecture of institutions of higher education. With answers to the question of ‘what is artificial intelligence’ shaped by philosophical positions taken since Aristotle, there is little agreement on an ultimate definition.

In 1950s, Alan Turing proposed a solution to the question of when a system designed by a human is ‘intelligent.’ Turing proposed the imitation game, a test that involves the capacity of a human listener to make the distinction of a conversation with a machine or another human; if this distinction is not detected, we can admit that we have an intelligent system, or artificial intelligence (AI). It is worth remembering that the focus on AI solutions goes back to 1950s; in 1956 John McCarthy offered one of the first and most influential definitions: “The study [of artificial intelligence] is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” (Russell and Norvig 2010 ).

Since 1956, we find various theoretical understandings of artificial intelligence that are influenced by chemistry, biology, linguistics, mathematics, and the advancements of AI solutions. However, the variety of definitions and understandings remains widely disputed. Most approaches focus on limited perspectives on cognition or simply ignore the political, psychological, and philosophical aspects of the concept of intelligence. For the purpose of our analysis of the impact of artificial intelligence in teaching and learning in higher education, we propose a basic definition informed by the literature review of some previous definitions on this field. Thus, we can define artificial intelligence (AI) as computing systems that are able to engage in human-like processes such as learning, adapting, synthesizing, self-correction and use of data for complex processing tasks.

Artificial intelligence is currently progressing at an accelerated pace, and this already impacts on the profound nature of services within higher education. For example, universities already use an incipient form of artificial intelligence, IBM’s supercomputer Watson. This solution provides student advice for Deakin University in Australia at any time of day throughout 365 days of the year (Deakin University 2014 ). Even if it is based on algorithms suitable to fulfill repetitive and relatively predictable tasks, Watson’s use is an example of the future impact of AI on the administrative workforce profile in higher education. This is changing the structure for the quality of services, the dynamic of time within the university, and the structure of its workforce. A supercomputer able to provide bespoke feedback at any hour is reducing the need to employ the same number of administrative staff previously serving this function. In this context, it is also important to note that ‘machine learning’ is a promising field of artificial intelligence. While some AI solutions remain dependent on programming, some have an inbuilt capacity to learn patterns and make predictions. An example is AlphaGo—a software developed by DeepMind, the AI branch of Google’s—that was able to defeat the world’s best player at Go, a very complex board game (Gibney 2017 ). We define ‘machine learning’ as a subfield of artificial intelligence that includes software able to recognize patterns, make predictions, and apply the newly discovered patterns to situations that were not included or covered by their initial design.

Results and discussion

As AI solutions have the potential to structurally change university administrative services, the realm of teaching and learning in higher education presents a very different set of challenges. Artificial intelligence solutions relate to tasks that can be automated, but cannot be yet envisaged as a solution for more complex tasks of higher learning. The difficulty of supercomputers to detect irony, sarcasm, and humor is marked by various attempts that are reduced to superficial solutions based on algorithms that can search factors such as a repetitive use of punctuations marks, use of capital letters or key phrases (Tsur et al. 2010 ). There is a new hype about possibilities of AI in education, but we have reasons to stay aware of the real limits of AI algorithmic solutions in complex endeavors of learning in higher education.

For example, we can remember that the enthusiastic and unquestioned trust in the AI capabilities of a revolutionary new car led on May 2016 to the death of the driver, when the car set on ‘autopilot’ went underneath a tractor-trailer that was not detected by the software (Reuters/ABC 2016 ). There is also the story of Microsoft’s embarrassing mistake to trust the AI-powered bot named Tay to go unsupervised on Twitter. Confident on the bot capacity to operate independently, Microsoft discovered that Tay turned fast into a racist, bigoted, and hate-spewing account. ‘Tay’ had to be shut down by Microsoft after only 16 h of work. For example, Tay answered the question “Are you a racist?” with a disturbing “because ur mexican”. A Microsoft spokesperson explained that: “The AI chatbot Tay is a machine learning project, designed for human engagement. It is as much a social and cultural experiment, as it is technical. Unfortunately, within the first 24 hours of coming online, we became aware of a coordinated effort by some users to abuse Tay’s commenting skills to have Tay respond in inappropriate ways. As a result, we have taken Tay offline and are making adjustments.” (Perez 2016 ).

There is consistent evidence—some presented in this paper—that AI solutions open a new horizon of possibilities for teaching and learning in higher education. However, it is important to admit the current limits of technology and admit that AI is not (yet) ready to replace teachers, but is presenting the real possibility to augment them. We are now seeing computing algorithms impacting on the most mundane aspects of daily life, from individuals’ credit scores to employability. Higher education is placed at the center of this profound change, which brings with it both extraordinary opportunities and risks. This important crossroad requires careful consideration and analysis from an academic perspective, especially as we can find tendencies to look at technological progress as a solution or replacement for sound pedagogical solutions or good teaching. The real potential of technology in higher education is—when properly used—to extend human capabilities and possibilities of teaching, learning, and research. The purpose of this paper is to kindle scholarly discussions on the evolving field of artificial intelligence in higher education. This stays aligned with some of the most ambitious research agendas in the field, such as the “National Artificial Intelligence Research and Development Strategic Plan,” released by the US President Barack Obama in October 2016. The Report states that “the walls between humans and AI systems are slowly beginning to erode, with AI systems augmenting and enhancing human capabilities. Fundamental research is needed to develop effective methods for human-AI interaction and collaboration” (U.S. National Science and Technology Council 2016 ).

As we note that significant advances in machine learning and artificial intelligence open new possibilities and challenges for higher education, it is important to observe that education is eminently a human-centric endeavor, not a technology centric solution. Despite rapid advancements in AI, the idea that we can solely rely on technology is a dangerous path, and it is important to maintain focus on the idea that humans should identify problems, critique, identify risks, and ask important questions that can start from issues such as privacy, power structures, and control to the requirement of nurturing creativity and leaving an open door to serendipity and unexpected paths in teaching and learning. The hype on AI can lead to an unquestioned panacea that can leave many who are on their path to higher learning under the wheels of reality, such as that tragic event of the driver led under a truck by what was considered to be a matchless software. Maintaining academic skepticism on this issue is especially important in education, as this is an act that can be reduced to information delivery and recollection; we need to maintain its aim to build educated minds and responsible citizens that are attached to general values of humanism.

The role of technology in higher learning is to enhance human thinking and to augment the educational process, not to reduce it to a set of procedures for content delivery, control, and assessment. With the rise of AI solutions, it is increasingly important for educational institutions to stay alert and see if the power of control over hidden algorithms that run them is not monopolized by tech-lords. Frank Pasquale notes in his seminal book ‘The Black Box Society’ that “Decisions that used to be based on human reflection are now made automatically. Software encodes thousands of rules and instructions computed in a fraction of a second” (Pasquale 2015 ). Pasquale is revealing in his book that we do not only have a quasi-concentrated and powerful monopoly over these solutions, but also an intentional lack of transparency on algorithms and how they are used. This is presented casually as a normal state of facts, the natural arrangements of Internet era, but it translates to highly dangerous levels of unquestioned power. Those who control algorithms that run AI solutions have now unprecedented influence over people and every sector of a contemporary society. The internal architecture of the mega-corporations such as Facebook or Google is not following a democratic model, but those of benevolent dictators who know what is best and decide with no consultation with their internal or external subjects. The monopoly and the strong control over sources of information, stifling critique and silencing de facto through invisibilisation views that are not aligned with interest and narratives promoted by techlords’ interests stand in direct opposition with higher learning. Universities have a role if they encourage dissent and open possibilities revealed by it. Higher learning is withering when the freedom of thinking and inquiry is suppressed in any form, as manipulations and the limitation of knowledge distorts and cancel in-depth understandings and the advancement of knowledge. If we reach a point where the agenda of universities is set by a handful of techlords, as well as the control over their information and the ethos of universities, higher education is looking ahead a very different age. The set of risks is too important to be overlooked and not explored with courage and careful analysis.

At the same time, the rapid advancements of AI are doubled by the effort of defunded universities to find economic solutions to balance depleted budgets. AI already presents the capability to replace a large number of administrative staff and teaching assistants in higher education. It is therefore important to explore the effects of these factors on learning in higher education, especially in the context of an increasing demand for initiative, creativity, and ‘entrepreneurial spirit’ for graduates. This paper opens an inquiry into the influence of artificial intelligence (AI) on teaching and learning and higher education. It also operates as an exploratory analysis of literature and recent studies on how AI can change not only how students learn in universities, but also on the entire architecture of higher education.

The rise of artificial intelligence and augmentation in higher education

The introduction and adoption of new technologies in learning and teaching has rapidly evolved over the past 30 years. Looking through the current lens, it is easy to forget the debates that have raged in our institutions over students being allowed to use what are now regarded as rudimentary technologies. In a longitudinal study of accommodations for students with a disability conducted between 1993 and 2005 in the USA, authors remind us of how contentious the debate was surrounding the use of the calculators and spell check programs for students with a disability none-the-less the general student body (Lazarus et al. 2008 ). Assistive technologies—such as text to speech, speech to text, zoom capacity, predictive text, spell checkers, and search engines—are just some examples of technologies initially designed to assist people with a disability. The use of these technological solutions was later expanded, and we find them now as generic features in all personal computers, handheld devices or wearable devices. These technologies now augment the learning interactions of all students globally, enhancing possibilities opened for teaching and design of educational experiences.

Moreover, artificial intelligence (AI) is now enhancing tools and instruments used day by day in cities and campuses around the world. From Internet search engines, smartphone features and apps, to public transport and household appliances. For example, the complex set of algorithms and software that power iPhone’s Siri is a typical example of artificial intelligence solutions that became part of everyday experiences (Bostrom and Yudkowsky 2011 ; Luckin 2017 ). Even if Apple’s Siri is labeled as a low complexity AI solution or simply a voice controlled computer interface, it is important to remember that it started as an artificial intelligence project funded in the USA by the Defense Advanced Research Projects Agency (DARPA) since 2001. This project was turned a year later into a company that was acquired by Apple, which integrated the application in its iPhone operation system in 2007. Google is using AI for its search engines and maps, and all new cars use AI from engine to breaks and navigation. Self-driving technology is already advanced, and some major companies are making this a top priority for development, such as Tesla, Volvo, Mercedes, and Google (Hillier et al. 2015 ) and trials on public roads in Australia commenced in 2015. Remarkably, a mining corporation is already taking advantage of self-driving technologies, now using self-driving trucks for two major exploitations in Western Australia (Diss 2015 ).

Personalized solutions are also closer than we imagined: ‘new scientist’ presented at the end of 2015 the initiative of Talkspace and IBM’s Watson to use artificial intelligence in psychotherapy (Rutkin 2015 ). This seems to be a major step towards changing the complex endeavor of education with AI. In fact, Nick Bostrom, Director of the Future of Humanity Institute at the UK’s Oxford University, observed since 2006 that artificial intelligence is now an integral part of our daily life: “A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it’s not labelled AI anymore” (Bostrom 2006 ). Again, very few people identify today Siri as a typical example of artificial intelligence and more as an algorithm-based personal assistant that is part of everyday life experiences. Given their increasing role within the global digital infrastructure, this also begs the question as to how algorithms are conceived of as we prepare ourselves for a range of different possible futures.

Students are placed now at the forefront of a vast array of possibilities and challenges for learning and teaching in higher education. Solutions for human-AI interaction and collaboration are already available to help people with disabilities. They can inspire educators to apply them in education to augment learners and teachers for a more engaging process. Carl Mitcham describes in his Encyclopedia of Science, Technology and Ethics a cyborg as “a crossbreed of a human and a machine” (Mitcham 2005 ). The idea of cyborgs is not as far away as we may imagine, as the possibilities to combine human capacities with new technologies are already being used and developed at an accelerated pace. For example, Hugh Herr, who is directing the Biomechatronics group at the MIT Media Lab and works with the Harvard–MIT Division of Health Sciences and Technology, recently observed in an interview for ‘new scientist’ that “…disability will end, I’d say, by the end of this century. And I think that’s a very conservative statement. At the rate technology is progressing, most disability will be gone in 50 years” (De Lange 2015 , p. 25). This company is producing technologically advanced prosthetics and exoskeletons, pioneering bionic technology for people with or without a disability. He notes that his research group developed an interface that “uses biology to close the loop between human and machine […] Imagine a world where our physicality doesn’t decrease as we age” (De Lange 2015 , p. 24). Complex computing systems using machine learning algorithms can serve people with all types of abilities and engage to a certain degree in human-like processes and complex processing tasks that can be employed in teaching and learning. This opens to a new era for institutions of higher education.

This type of human-machine interface presents the immediate potential to change the way we learn, memorize, access, and create information. The question of how long it will take to use this type of interface to enhance human memory and cognition is one which we are currently unable to answer. It may turn to reality beyond the end of this century, as the MIT scholar suggests or much sooner when we consider the pace of change in the technologies used in teaching and learning since 2007 when the first iPhone was launched. Since then, not only has the iPhone integrated breakthrough technologies that seemed impossible just a few years ago to how we access and use information (such as fingerprint identification and the ‘intelligent’ Siri assistant), but this technology has introduced a significant cultural shift that impacts on our everyday lives. Either way, if we shift the focus of ‘cyborgs’ from science-fiction to the idea of computer augmented capacity for teachers and students alike, it is not unrealistic to consider that cyborgs—or ‘crossbreeds’ of human and machines—will soon be a reality in teaching and research in universities of the near future.

The impact of artificial intelligence is already visible in the world economy and has captured the attention of many analysts. The largest investment ever made by Google in the European Union is the acquisition in 2014 of DeepMind technologies, with $400 million. DeepMind Technologies, now named Google DeepMind, is a London-based artificial intelligence startup specialized in machine learning and advanced algorithms. Notably, Google also made significant investments in the German Research Centre for Artificial Intelligence (DFKI GmbH), which is, according to their website, “the biggest research center worldwide in the area of Artificial Intelligence and its application, in terms of number of employees and the volume of external funds” (DFKI 2015 ). Tech giants like Apple, Google, Microsoft, and Facebook currently compete in the field of artificial intelligence and are investing heavily in new applications and research. Google announced in December 2015 that the company’s quantum computer called D-Wave 2X will be used for complex operations of AI, generically referred to as optimization problems (Neven 2015 ). This new machine is 100 million times faster than any other contemporary computers, a serious leap ahead for AI, considered by Google researchers as a significant breakthrough: “We hope it helps researchers construct more efficient and more accurate models for everything from speech recognition, to web search, to protein folding” (Neven 2013 ).

This wave of interest and investments in artificial intelligence will soon impact on universities. Most likely, financial pressures related to the large numbers of students currently undertaking higher education driven by the goal of democratization of higher education, and the international student market will stand as a compelling reason to seek out AI solutions. The ‘outsourcing’ of the academic workforce, in terms of numbers of academics employed and tenured positions, is now open to a massive takeover by intelligent machines (Grove 2015 ). ‘Massification’ of higher education and the political call to cut public funding for universities translates into a real need to cut costs. With research still being the main source of funds and prestige in international rankings, the MOOC hype unveiled the tempting solution for many university administrators to cut costs by reducing expensive academic teaching staff. This shift is currently being aggressively pursued in Australian universities, with a constant shift towards casual and short-term contracts; in a study conducted by L.H. Martin Institute it is documented that “…there is an escalating trend in the number and percentage of academic staff on contingent appointments, and a declining trend in the percentage of academic staff with continuing appointments who undertake both teaching and research” (Andrews et al. 2016 ). In the UK, we find various initiatives following the same trend, such as that of University of Warwick, which created a new department to employ all casual teaching staff to outsource teaching. This new department was established to function in a way “similar to another subsidiary used to pay cleaners and catering staff, suitable to serve the University of Warwick and also sell teaching and assessment services to other institutions” (Gallagher 2015 ).

As examples presented in previous page show, the “crossbreed” of the human brain and a machine is already possible, and this will essentially challenge teachers to find new dimensions, functions, and radically new pedagogies for a different context for learning and teaching. For example, brain-computer interfaces (BCIs), that captured the imagination of researchers across the world, are currently recording significant advances. Using brain signals with various recording and analysis methods, along with innovative technological approaches for new computing systems, specialists in the field now provide feasible solutions to remotely control software with a brain-computer interface (Andrea et al. 2015 ). BCIs are now able to capture and decode brain activity to enable communication and control by individuals with motor function disabilities (Wolpaw and Wolpaw 2012 ). Kübler et al. observe that at this point “studies have demonstrated fast and reliable control of brain-computer interfaces (BCIs) by healthy subjects and individuals with neurodegenerative disease alike” (Kübler et al. 2015 ). The concept of humanity and the possibilities of humans stand currently to be redefined by technology with unprecedented speed: technology is quickly expanding the potential to use AI functions to enhance our skills and abilities. As Andreas Schleicher observed, “Innovation in education is not just a matter of putting more technology into more classrooms; it is about changing approaches to teaching so that students acquire the skills they need to thrive in competitive global economies” (Schleicher 2015 ).

Past lessons, possibilities, and challenges of AI solutions

Widening participation in higher education and the continuous increase in the number of students, class sizes, staff costs, and broader financial pressures on universities makes the use of technology or teacherbots a very attractive solution. This became evident when massive open online courses (MOOCs) enlightened the imagination of many university administrators. The understanding of “open courses” is that no entry requirements or fees were required, and online students could enroll and participate from any country in the world with internet access. Both of these factors enabled universities to market globally for students, resulting in massive enrolment numbers. The promise was generous, but it soon became evident that one of the problems created for teachers was their human capacity to actively engage with massive numbers of diverse students studying globally from different time zones, at different rates of progress and with different frames of reference and foundational skills for the course that they are studying. Assisting students in large classes to progress effectively through their learning experience to achieve desired outcomes, conduct assessments, and provide constructive personalized feedback remained unsolved issues. Sian Bayne makes the observation in Teacherbot: Interventions in Automated Teaching , that the current perspective of using automated methods in teaching “are driven by a productivity-oriented solutionism,” not by pedagogical or charitable reasoning, so we need to re-explore a humanistic perspective for mass education to replace the “cold technocratic imperative” (Bayne 2015 ). Bayne speaks from the experience of meeting the need created by the development and delivery of a massive open online course by the University of Edinburgh. This course had approximately 90,000 students from 200 countries enrolled.

The lesson of MOOCs is important and deserves attention. Popenici and Kerr observed that MOOCs were first used in 2008 and since then: “…we have been hearing the promise of a tsunami of change that is coming over higher education. It is not uncommon with a tsunami to see people enticed by the retreat of the waters going to collect shells, thinking that this is the change that is upon them. Tragically, the real change is going to come in the form of a massive wave under which they will perish as they play on the shores. Similarly, we need to take care that we are not deluded to confuse MOOCs, which are figuratively just shells on the seabed, with the massive wall of real change coming our way” (Popenici and Kerr 2013 ). It is becoming clear in 2016 that MOOCs remain just a different kind of online course, interesting and useful, but not really aimed at or capable of changing the structure and function of universities. Research and data on this topic reflect the failure of MOOCs to deliver on their proponents’ promises. More importantly, the unreserved and irrational hype that surrounded MOOCs is a when decision-makers in academia decided to ignore all key principles—such as evidence-based arguments or academic skepticism—and embrace a fad sold by Silicon Valley venture capitalists with no interests in learning other than financial profits. As noted in a recent book chapter “this reckless shift impacts on the sustainability of higher learning in particular and of higher education by and large” (Popenici 2015 ).

There are solid arguments—some cited above in this paper—to state that it is more realistic to consider the impact of machine learning in higher education as the real wave of change. In effect, lessons of the past show why it is so important to avoid the same mistakes revealed by the past fads or to succumb to a convenient complacency that is serving only the agenda of companies that are in search of new (or bigger) markets. Online learning proved very often the potential to successfully help institutions of higher education reach some of the most ambitious goals in learning, teaching, and research. However, the lesson of MOOCs is also that a limited focus on one technology solution without sufficient evidence-based arguments can become a distraction for education and a perilous pathway for the financial sustainability of these institutions.

Higher education is now taking its first steps into the unchartered territory of the possibilities opened by AI in teaching, learning, and higher education organization and governance. Implications and possibilities of these technological advances can already be seen. By way of example, recent advancements in non-invasive brain-computer interfaces and artificial intelligence are opening new possibilities to rethink the role of the teacher, or make steps towards the replacement of teachers with teacher-robots, virtual “teacherbots” (Bayne 2015 ; Botrel et al. 2015 ). Providing affordable solutions to use brain computer interface (BCI) devices capable to measure when a student is fully focused on the content and learning tasks (Chen et al. 2015 ; González et al. 2015 ) is already possible, and super-computers, such as IBM’s Watson, can provide an automated teacher presence for the entire duration of a course. The possibility to communicate and command computers through thought and wider applications of AI in teaching and learning represents the real technological revolution that will dramatically change the structure of higher education across the world. Personalized learning with a teacherbot, or ‘cloud-lecturer’, can be adopted for blended delivery courses or fully online courses. Teacherbots—computing solutions for the administrative part of teaching, dealing mainly with content delivery, basic and administrative feedback and supervision—are already presenting as a disruptive alternative to traditional teaching assistants. An example is offered by the course offered by Professor Ashok Goel on knowledge-based artificial intelligence (KBAI) in the online Master in Computer Sciences program, at Georgia Tech in the USA. The teaching assistant was so valued by students that one wanted to nominate her to the outstanding TA award. This TA managed to meet the highest expectations of students. The surprise at the end of the course was to find out that Jill Watson was not a real person, but a teacherbot, a virtual teaching assistant was based on the IBM’s Watson platform (Maderer 2016 ).

This enlightened the imaginations of many, reaching international news across the world and respected media outlets such as The New Your Times or The Washington Post . However, we must be careful when we see the temptation to equate education with solutions provided by algorithms. There are widespread implications for the advancement of AI to the point where a computer can serve as a personalized tutor able to guide and manage students’ learning and engagement. This opens to the worrying possibility to see a superficial, but profitable, approach where teaching is replaced by AI automated solutions. Especially as we are at a point where we need to find a new pedagogical philosophy that can help students achieve the set of skills required in the twenty-first century for a balanced civic, economic, and social life. We have a new world that is based on uncertainty and challenges that change at a rapid pace, and all this requires creativity, flexibility, the capacity to use and adapt to uncertain contexts. Graduates have to act in a world of value conflicts, information limitations, vast registers of risks, and radical uncertainty. All this, along with the ongoing possibility of staying within personal and group ‘bubbles’ of and being exposed to vast operations of manipulation require a new thinking about the use of technology in education and a new set of graduate attributes. As advanced as AI solutions may become we cannot yet envisage a future where algorithms can really replace the complexity of human mind. For certain, current developments show that it is highly unlikely to happen in the next decade, despite a shared excessive optimism. The AI hype is not yet double by results; for example, Ruchir Puri, the Chief Architect of Watson, IBM’s AI supercomputer, recently noted that “There is a lot of hype around AI, but what it can’t do is very big right now. What it can do is very small.”

This reality may encourage policy-makers and experts to reimagine institutions of higher education in an entirely new paradigm, much more focused on imagination, creativity, and civic engagement. With the capacity to guide learning, monitor participation, and student engagement with the content, AI can customize the ‘feed’ of information and materials into the course according to learner’s needs, provide feedback and encouragement. However, teachers can use this to prepare students to a world of hyper-complexity where the future is not reduced to the simple aim of ‘employability.’ Teacherbots are already presenting as a disruptive alternative to traditional teaching staff, but it is very important to inquire at this point how do we use them for the benefit of students in the context of a profound rethink of what is currently labeled as ‘graduate attributes’ (Mason et al. 2016 ).

Even if in 2017 we find little and exploration of what is a teacherbot and what their capabilities are possible now and in a predictable future, AI technology has slipped into the backdoor of all our lives and this is imposing a much more focused research in higher education. AI solutions are currently monitoring our choices, preferences, movements, measuring strengths, and weaknesses, providing feedback, encouragement, badges, comparative analytics, customized news feeds, alerts, predictive text, so they are project managing our lives. At this point, we can see a teacherbot as a complex algorithmic interface able to use artificial intelligence for personalized education, able to provide bespoke content, supervision, and guidance for students and help for teachers. Teacherbots are defined as any machine-based software or hardware that assumes the role traditionally performed by a teacher assistant in organizing information and providing fast answers to a wide set of predictable questions; it can be facilitating, monitoring, assessing, and managing student learning within the online learning space. These solutions are closer than many academics may think. Tinkering with the old system of transmitting information to passive students, in class or in front of computers, is open to disruption from a highly personalized, scaleable, and affordable alternative AI solutions, such as ‘Jill Watson.’ While contact time and personal guidance by faculty may be should be retained not only in some elite institutions of higher education, as this will define the quality of education, but intelligent machines can be used by all to meet the learning and support needs of massive numbers of students.

The rise of AI makes it impossible to ignore a serious debate about its future role of teaching and learning in higher education and what type of choices universities will make in regard to this issue. The fast pace of technology innovation and the associated job displacement, acknowledged widely by experts in the field (source), implies that teaching in higher education requires a reconsideration of teachers’ role and pedagogies. The current use of technological solutions such as ‘learning management systems’ or IT solutions to detect plagiarism already raise the question of who sets the agenda for teaching and learning: corporate ventures or institutions of higher education? The rise of techlords and the quasi-monopoly of few tech giants also come with questions regarding the importance of privacy and the possibility of a dystopian future. These issues deserve a special attention as universities should include this set of risks when thinking about a sustainable future.

Moreover, many sets of tasks that are currently placed at the core of teaching practice in higher education will be replaced by AI software based on complex algorithms designed by programmers that can transmit their own biases or agendas in operating systems. An ongoing critique and inquiry in proposed solutions stay critical to guarantee that universities remain institutions able to maintain civilization, promote, and develop knowledge and wisdom.

In effect, now is the time for universities to rethink their function and pedagogical models and their future relation with AI solutions and their owners. Furthermore, institutions of higher education see ahead the vast register of possibilities and challenges opened by the opportunity to embrace AI in teaching and learning. These solutions present new openings for education for all, while fostering lifelong learning in a strengthened model that can preserve the integrity of core values and the purpose of higher education.

We consider that there is a need for research on the ethical implications of the current control on developments of AI and the possibility to wither the richness of human knowledge and perspectives with the monopoly of few entities. We also believe that it is important to focus further research on the new roles of teachers on new learning pathways for higher degree students, with a new set of graduate attributes, with a focus on imagination, creativity, and innovation; the set of abilities and skills that can hardly be ever replicated by machines.

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Dr. Stefan Popenici is working at Charles Darwin University as Senior Lecturer in Higher Education and is an Honorary Fellow of the Melbourne Graduate School of Education at the University of Melbourne. He is also Associate Director of the Imaginative Education Research Group at Simon Fraser University, Canada. He is an academic with extensive work experience in teaching and learning, governance, research, training, and academic development with universities in Europe, North America, South East Asia, New Zealand, and Australia. Dr. Popenici was a Senior Advisor of Romania’s Minister of Education on educational reform and academic research, a Senior Consultant of the President of De La Salle University Philippines on scholarship and research, and Expert Consultant for various international institutions in education (e.g., Fulbright Commission, Council of Europe). For his exceptional contributions to education and research and strategic leadership, the President of Romania knighted Stefan in the Order “Merit of Education.”

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Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021

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In the past few decades, technology has completely transformed the world around us. Indeed, experts believe that the next big digital transformation in how we live, communicate, work, trade and learn will be driven by Artificial Intelligence (AI) [ 83 ]. This paper presents a high-level industrial and academic overview of AI in Education (AIEd). It presents the focus of latest research in AIEd on reducing teachers’ workload, contextualized learning for students, revolutionizing assessments and developments in intelligent tutoring systems. It also discusses the ethical dimension of AIEd and the potential impact of the Covid-19 pandemic on the future of AIEd’s research and practice. The intended readership of this article is policy makers and institutional leaders who are looking for an introductory state of play in AIEd.

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1 Introduction

Artificial Intelligence (AI) is changing the world around us [ 42 ]. As a term it is difficult to define even for experts because of its interdisciplinary nature and evolving capabilities. In the context of this paper, we define AI as a computer system that can achieve a particular task through certain capabilities (like speech or vision) and intelligent behaviour that was once considered unique to humans [ 54 ]. In more lay terms we use the term AI to refer to intelligent systems that can automate tasks traditionally carried out by humans. Indeed, we read AI within the continuation of the digital age, with increased digital transformation changing the ways in which we live in the world. With such change the skills and knowhow of people must reflect the new reality and within this context, the World Economic Forum identified sixteen skills, referred to as twenty-first century skills necessary for the future workforce [ 79 ]. This includes skills such as technology literacy, communication, leadership, curiosity, adaptability, etc. These skills have always been important for a successful career, however, with the accelerated digital transformation of the past 2 years and the focus on continuous learning in most professional careers, these skills are becoming necessary for learners.

AI will play a very important role in how we teach and learn these new skills. In one dimension, ‘AIEd’ has the potential to dramatically automate and help track the learner’s progress in all these skills and identify where best a human teacher’s assistance is needed. For teachers, AIEd can potentially be used to help identify the most effective teaching methods based on students’ contexts and learning background. It can automate monotonous operational tasks, generate assessments and automate grading and feedback. AI does not only impact what students learn through recommendations, but also how they learn, what are the learning gaps, which pedagogies are more effective and how to retain learner’s attention. In these cases, teachers are the ‘human-in-the-loop’, where in such contexts, the role of AI is only to enable more informed decision making by teachers, by providing them predictions about students' performance or recommending relevant content to students after teachers' approval. Here, the final decision makers are teachers.

Segal et al. [ 58 ] developed a system named SAGLET that utilized ‘human-in-the-loop’ approach to visualize and model students’ activities to teachers in real-time enabling them to intervene more effectively as and when needed. Here the role of AI is to empower the teachers enabling them to enhance students’ learning outcomes. Similarly, Rodriguez et al. [ 52 ] have shown how teachers as ‘human-in-the-loop’ can customize multimodal learning analytics and make them more effective in blended learning environments.

Critically, all these achievements are completely dependent on the quality of available learner data which has been a long-lasting challenge for ed-tech companies, at least until the pandemic. Use of technology in educational institutions around the globe is increasing [ 77 ], however, educational technology (ed-tech) companies building AI powered products have always complained about the lack of relevant data for training algorithms. The advent and spread of Covid in 2019 around the world pushed educational institutions online and left them at the mercy of ed-tech products to organize content, manage operations, and communicate with students. This shift has started generating huge amounts of data for ed-tech companies on which they can build AI systems. According to a joint report: ‘Shock to the System’, published by Educate Ventures and Cambridge University, optimism of ed-tech companies about their own future increased during the pandemic and their most pressing concern became recruitment of too many customers to serve effectively [ 15 ].

Additionally, most of the products and solutions provided by ed-tech start-ups lack the quality and resilience to cope with intensive use of several thousands of users. Product maturity is not ready for huge and intense demand as discussed in Sect. “ Latest research ” below. We also discuss some of these products in detail in Sect. “ Industry’s focus ” below. How do we mitigate the risks of these AI powered products and who monitors the risk? (we return to this theme in our discussion of ethics—Sect. “ Ethical AIEd ”).

This paper is a non-exhaustive overview of AI in Education that presents a brief survey of the latest developments of AI in Education. It begins by discussing different aspects of education and learning where AI is being utilized, then turns to where we see the industry’s current focus and then closes with a note on ethical concerns regarding AI in Education. This paper also briefly evaluates the potential impact of the pandemic on AI’s application in education. The intended readership of this article is the policy community and institutional executives seeking an instructive introduction to the state of play in AIEd. The paper can also be read as a rapid introduction to the state of play of the field.

2 Latest research

Most work within AIEd can be divided into four main subdomains. In this section, we survey some of the latest work in each of these domains as case studies:

Reducing teachers’ workload: the purpose of AI in Education is to reduce teachers’ workload without impacting learning outcomes

Contextualized learning for students: as every learner has unique learning needs, the purpose of AI in Education is to provide customized and/or personalised learning experiences to students based on their contexts and learning backgrounds.

Revolutionizing assessments: the purpose of AI in Education is to enhance our understanding of learners. This not only includes what they know, but also how they learn and which pedagogies work for them.

Intelligent tutoring systems (ITS): the purpose of AI in Education is to provide intelligent learning environments that can interact with students, provide customized feedback and enhance their understanding of certain topics

2.1 Reducing teachers’ workload

Recent research in AIEd is focusing more on teachers than other stakeholders of educational institutions, and this is for the right reasons. Teachers are at the epicenter of every learning environment, face to face or virtual. Participatory design methodologies ensure that teachers are an integral part of the design of new AIEd tools, along with parents and learners [ 45 ]. Reducing teachers’ workload has been a long-lasting challenge for educationists, hoping to achieve more affective teaching in classrooms by empowering the teachers and having them focus more on teaching than the surrounding activities.

With the focus on online education during the pandemic and emergence of new tools to facilitate online learning, there is a growing need for teachers to adapt to these changes. Importantly, teachers themselves are having to re-skill and up-skill to adapt to this age, i.e. the new skills that teachers need to develop to fully utilize the benefits of AIEd [ 39 ]. First, they need to become tech savvy to understand, evaluate and adapt new ed-tech tools as they become available. They may not necessarily use these tools, but it is important to have an understanding of what these tools offer and if they share teachers’ workload. For example, Zoom video calling has been widely used during the pandemic to deliver lessons remotely. Teachers need to know not only how to schedule lessons on Zoom, but also how to utilize functionalities like breakout rooms to conduct group work and Whiteboard for free style writing. Second, teachers will also need to develop analytical skills to interpret the data that are visualized by these ed-tech tools and to identify what kind of data and analytics tools they need to develop a better understanding of learners. This will enable teachers to get what they exactly need from ed-tech companies and ease their workload. Third, teachers will also need to develop new team working, group and management skills to accommodate new tools in their daily routines. They will be responsible for managing these new resources most efficiently.

Selwood and Pilkington [ 61 ] showed that the use of Information and Communication Technologies (ICT) leads to a reduction in teachers’ workload if they use it frequently, receive proper training on how to use ICT and have access to ICT in home and school. During the pandemic, teachers have been left with no options other than online teaching. Spoel et al. [ 76 ] have shown that the previous experience with ICT did not play a significant role in how they dealt with the online transition during pandemic. Suggesting that the new technologies are not a burden for teachers. It is early to draw any conclusions on the long-term effects of the pandemic on education, online learning and teachers’ workload. Use of ICT during the pandemic may not necessarily reduce teacher workload, but change its dynamics.

2.2 Contextualized learning for students

Every learner has unique learning contexts based on their prior knowledge about the topic, social background, economic well-being and emotional state [ 41 ]. Teaching is most effective when tailored to these changing contexts. AIEd can help in identifying the learning gaps in each learner, offer content recommendations based on that and provide step by step solutions to complex problems. For example, iTalk2Learn is an opensource platform that was developed by researchers to support math learning among students between 5 and 11 years of age [ 22 ]. This tutor interacted with students through speech, identified when students were struggling with fractions and intervened accordingly. Similarly, Pearson has launched a calculus learning tool called AIDA that provides step by step guidance to students and helps them complete calculus tasks. Use of such tools by young students also raises interesting questions about the illusion of empathy that learners may develop towards such educational bots [ 73 ].

Open Learner Models [ 12 , 18 ] have been widely used in AIEd to facilitate learners, teachers and parents in understanding what learners know, how they learn and how AI is being used to enhance learning. Another important construct in understanding learners is self-regulated learning [ 10 , 68 ]. Zimmerman and Schunk [ 85 ] define self-regulated learning as learner’s thoughts, feelings and actions towards achieving a certain goal. Better understanding of learners through open learner models and self-regulated learning is the first step towards contextualized learning in AIEd. Currently, we do not have completely autonomous digital tutors like Amazon’s Alexa or Apple’s Siri for education but domain specific Intelligent Tutoring Systems (ITS) are also very helpful in identifying how much students know, where they need help and what type of pedagogies would work for them.

There are a number of ed-tech tools available to develop basic literacy skills in learners like double digit division or improving English grammar. In future, AIEd powered tools will move beyond basic literacy to develop twenty-first century skills like curiosity [ 49 ], initiative and creativity [ 51 ], collaboration and adaptability [ 36 ].

2.3 Revolutionizing assessments

Assessment in educational context refers to ‘any appraisal (or judgement or evaluation) of a student’s work or performance’ [ 56 ]. Hill and Barber [ 27 ] have identified assessments as one of the three pillars of schooling along with curriculum and learning and teaching. The purpose of modern assessments is to evaluate what students know, understand and can do. Ideally, assessments should take account of the full range of student abilities and provide useful information about learning outcomes. However, every learner is unique and so are their learning paths. How can standardized assessment be used to evaluate every student, with distinct capabilities, passions and expertise is a question that can be posed to broader notions of educational assessment. According to Luckin [ 37 ] from University College London, ‘AI would provide a fairer, richer assessment system that would evaluate students across a longer period of time and from an evidence-based, value-added perspective’.

AIAssess is an example of an intelligent assessment tool that was developed by researchers at UCL Knowledge lab [ 38 , 43 ]. It assessed students learning math and science based on three models: knowledge model, analytics model and student model. Knowledge component stored the knowledge about each topic, the analytics component analyzed students’ interactions and the student model tracked students’ progress on a particular topic. Similarly, Samarakou et al. [ 57 ] have developed an AI assessment tool that also does qualitative evaluation of students to reduce the workload of instructors who would otherwise spend hours evaluating every exercise. Such tools can be further empowered by machine learning techniques such as semantic analysis, voice recognition, natural language processing and reinforcement learning to improve the quality of assessments.

2.4 Intelligent tutoring systems (ITS)

An intelligent tutoring system is a computer program that tries to mimic a human teacher to provide personalized learning to students [ 46 , 55 ]. The concept of ITS in AIEd is decades old [ 9 ]. There have always been huge expectations from ITS capabilities to support learning. Over the years, we have observed that there has been a significant contrast between what ITS were envisioned to deliver and what they have actually been capable of doing [ 4 ].

A unique combination of domain models [ 78 ], pedagogical models [ 44 ] and learner models [ 20 ] were expected to provide contextualized learning experiences to students with customized content, like expert human teachers [ 26 , 59 , 65 ],. Later, more models were introduced to enhance students' learning experience like strategy model, knowledge-base model and communication model [ 7 ]. It was expected that an intelligent tutoring system would not just teach, but also ensure that students have learned. It would care for students [ 17 ]. Similar to human teachers, ITS would improve with time. They would learn from their experiences, ‘understand’ what works in which contexts and then help students accordingly [ 8 , 60 ].

In recent years, ITS have mostly been subject and topic specific like ASSISTments [ 25 ], iTalk2Learn [ 23 ] and Aida Calculus. Despite being limited in terms of the domain that a particular intelligent tutoring system addresses, they have proven to be effective in providing relevant content to students, interacting with students [ 6 ] and improving students’ academic performance [ 18 , 41 ]. It is not necessary that ITS would work in every context and facilitate every teacher [ 7 , 13 , 46 , 48 ]. Utterberg et al. [78] showed why teachers have abandoned technology in some instances because it was counterproductive. They conducted a formative intervention with sixteen secondary school mathematics teachers and found systemic contradictions between teachers’ opinions and ITS recommendations, eventually leading to the abandonment of the tool. This highlights the importance of giving teachers the right to refuse AI powered ed-tech if they are not comfortable with it.

Considering a direct correlation between emotions and learning [ 40 ] recently, ITS have also started focusing on emotional state of students while learning to offer a more contextualized learning experience [ 24 ].

2.5 Popular conferences

To reflect on the increasing interest and activity in the space of AIEd, some of the most popular conferences in AIEd are shown in Table 1 below. Due to the pandemic all these conferences will be available virtually in 2021 as well. The first international workshop on multimodal artificial intelligence in education is being organized at AIEd [74] conference to promote the importance of multimodal data in AIEd.

3 Industry’s focus

In this section, we introduce the industry focus in the area of AIEd by case-studying three levels of companies start-up level, established/large company and mega-players (Amazon, Cisco). These companies represent different levels of the ecosystem (in terms of size).

3.1 Start-ups

There have been a number of ed-tech companies that are leading the AIEd revolution. New funds are also emerging to invest in ed-tech companies and to help ed-tech start-ups in scaling their products. There has been an increase in investor interest [ 21 ]. In 2020 the amount of investment raised by ed-tech companies more than doubled compared to 2019 (according to Techcrunch). This shows another dimension of pandemic’s effect on ed-tech. With an increase in data coming in during the pandemic, it is expected that industry’s focus on AI powered products will increase.

EDUCATE, a leading accelerator focused on ed-tech companies supported by UCL Institute of Education and European Regional Development Fund was formed to bring research and evidence at the centre of product development for ed-tech. This accelerator has supported more than 250 ed-tech companies and 400 entrepreneurs and helped them focus on evidence-informed product development for education.

Number of ed-tech companies are emerging in this space with interesting business models. Third Space Learning offers maths intervention programs for primary and secondary school students. The company aims to provide low-cost quality tuition to support pupils from disadvantaged backgrounds in UK state schools. They have already offered 8,00,000 h of teaching to around 70,000 students, 50% of who were eligible for free meals. Number of mobile apps like Kaizen Languages, Duolingo and Babbel have emerged that help individuals in learning other languages.

3.2 Established players

Pearson is one of the leading educational companies in the world with operations in more than 70 countries and more than 22,000 employees worldwide. They have been making a transition to digital learning and currently generate 66% of their annual revenue from it. According to Pearson, they have built world’s first AI powered calculus tutor called Aida which is publicly available on the App Store. But, its effectiveness in improving students’ calculus skills without any human intervention is still to be seen.

India based ed-tech company known for creating engaging educational content for students raised investment at a ten billion dollar valuation last year [ 70 ]. Century tech is another ed-tech company that is empowering learning through AI. They claim to use neuroscience, learning science and AI to personalize learning and identifying the unique learning pathways for students in 25 countries. They make more than sixty thousand AI powered smart recommendations to learners every day.

Companies like Pearson and Century Tech are building great technology that is impacting learners across the globe. But the usefulness of their acclaimed AI in helping learners from diverse backgrounds, with unique learning needs and completely different contexts is to be proven. As discussed above, teachers play a very important role on how their AI is used by learners. For this, teacher training is vital to fully understand the strengths and weaknesses of these products. It is very important to have an awareness of where these AI products cannot help or can go wrong so teachers and learners know when to avoid relying on them.

In the past few years, the popularity of Massive Online Open Courses (MOOCS) has grown exponentially with the emergence of platforms like Coursera, Udemy, Udacity, LinkedIn Learning and edX [ 5 , 16 , 28 ]. AI can be utilized to develop a better understanding of learner behaviour on MOOCS, produce better content and enhance learning outcomes at scale. Considering these platforms are collecting huge amounts of data, it will be interesting to see the future applications of AI in offering personalized learning and life-long learning solutions to their users [ 81 ].

3.3 Mega-players

Seeing the business potential of AIEd and the kind of impact it can have on the future of humanity, some of the biggest tech companies around the globe are moving into this space. The shift to online education during the pandemic boosted the demand for cloud services. Amazon’s AWS (Amazon Web Services) as a leader in cloud services provider facilitated institutions like Instituto Colombiano para la Evaluacion de la Educacion (ICFES) to scale their online examination service for 70,000 students. Similarly, LSE utilized AWS to scale their online assessments for 2000 students [ 1 , 3 ].

Google’s CEO Sunder Pichai stated that the pandemic offered an incredible opportunity to re-imagine education. Google has launched more than 50 new software tools during the pandemic to facilitate remote learning. Google Classroom which is a part of Google Apps for Education (GAFE) is being widely used by schools around the globe to deliver education. Research shows that it improves class dynamics and helps with learner participation [ 2 , 29 , 62 , 63 , 69 ].

Before moving onto the ethical dimensions of AIEd, it is important to conclude this section by noting an area that is of critical importance to processing industry and services. Aside from these three levels of operation (start-up, medium, and mega companies), there is the question of development of the AIEd infrastructure. As Luckin [41] points out, “True progress will require the development of an AIEd infrastructure. This will not, however, be a single monolithic AIEd system. Instead, it will resemble the marketplace that has been developed for smartphone apps: hundreds and then thousands of individual AIEd components, developed in collaboration with educators, conformed to uniform international data standards, and shared with researchers and developers worldwide. These standards will enable system-level data collation and analysis that help us learn much more about learning itself and how to improve it”.

4 Ethical AIEd

With a number of mishaps in the real world [ 31 , 80 ], ethics in AI has become a real concern for AI researchers and practitioners alike. Within computer science, there is a growing overlap with the broader Digital Ethics [ 19 ] and the ethics and engineering focused on developing Trustworthy AI [ 11 ]. There is a focus on fairness, accountability, transparency and explainability [ 33 , 82 , 83 , 84 ]. Ethics in AI needs to be embedded in the entire development pipeline, from the decision to start collecting data till the point when the machine learning model is deployed in production. From an engineering perspective, Koshiyama et al. [ 35 ] have identified four verticals of algorithmic auditing. These include performance and robustness, bias and discrimination, interpretability and explainability and algorithmic privacy.

In education, ethical AI is crucial to ensure the wellbeing of learners, teachers and other stakeholders involved. There is a lot of work going on in AIEd and AI powered ed-tech tools. With the influx of large amounts of data due to online learning during the pandemic, we will most likely see an increasing number of AI powered ed-tech products. But ethics in AIEd is not a priority for most ed-tech companies and schools. One of the reasons for this is the lack of awareness of relevant stakeholders regarding where AI can go wrong in the context of education. This means that the drawbacks of using AI like discrimination against certain groups due to data deficiencies, stigmatization due to reliance on certain machine learning modelling deficiencies and exploitation of personal data due to lack of awareness can go unnoticed without any accountability.

An AI wrongly predicting that a particular student will not perform very well in end of year exams or might drop out next year can play a very important role in determining that student’s reputation in front of teachers and parents. This reputation will determine how these teachers and parents treat that learner, resulting in a huge psychological impact on that learner, based on this wrong description by an AI tool. One high-profile case of harm was in the use of an algorithm to predict university entry results for students unable to take exams due to the pandemic. The system was shown to be biased against students from poorer backgrounds. Like other sectors where AI is making a huge impact, in AIEd this raises an important ethical question regarding giving students the freedom to opt out of AI powered predictions and automated evaluations.

The ethical implications of AI in education are dependent on the kind of disruption AI is doing in the ed-tech sector. On the one hand, this can be at an individual level for example by recommending wrong learning materials to students, or it can collectively impact relationships between different stakeholders such as how teachers perceive learners’ progress. This can also lead to automation bias and issues of accountability [ 67 ] where teachers begin to blindly rely on AI tools and prefer the tool’s outcomes over their own better judgement, whenever there is a conflict.

Initiatives have been observed in this space. For example, Professor Rose Luckin, professor of learner centered design at University College London along with Sir Anthony Seldon, vice chancellor of the University of Buckingham and Priya Lakhani, founder and CEO of Century Tech founded the Institute of Ethical AI in Education (IEAIEd) [ 72 ] to create awareness and promote the ethical aspects of AI in education. In its interim report, the institute identified seven different requirements for ethical AI to mitigate any kind of risks for learners. This included human agency and oversight to double-check AI’s performance, technical robustness and safety to prevent AI going wrong with new data or being hacked; diversity to ensure similar distribution of different demographics in data and avoid bias; non-discrimination and fairness to prevent anyone from being unfairly treated by AI; privacy and data governance to ensure everyone has the right to control their data; transparency to enhance the understanding of AI products; societal and environmental well-being to ensure that AI is not causing any harm and accountability to ensure that someone takes the responsibility for any wrongdoings of AI. Recently, the institute has also published a framework [ 71 ] for educators, schools and ed-tech companies to help them with the selection of ed-tech products with various ethical considerations in mind, like ethical design, transparency, privacy etc.

With the focus on online learning during the pandemic, and more utilization of AI powered ed-tech tools, risks of AI going wrong have increased significantly for all the stakeholders including ed-tech companies, schools, teachers and learners. A lot more work needs to be done on ethical AI in learning contexts to mitigate these risks, including assessment balancing risks and opportunities.

UNESCO published ‘Beijing Consensus’ on AI and Education that recommended member states to take a number of actions for the smooth and positively impactful integration of AI with education [ 74 ]. International bodies like EU have also recently published a set of draft guidelines under the heading of EU AI Act to ban certain uses of AI and categorize some as ‘high risk’ [ 47 ].

5 Future work

With the focus on online education due to Covid’19 in the past year, it will be consequential to see what AI has to offer for education with vast amounts of data being collected online through Learning Management Systems (LMS) and Massive Online Open Courses (MOOCS).

With this influx of educational data, AI techniques such as reinforcement learning can also be utilized to empower ed-tech. Such algorithms perform best with the large amounts of data that was limited to very few ed-tech companies in 2021. These algorithms have achieved breakthrough performance in multiple domains including games [ 66 ], healthcare [ 14 ] and robotics [ 34 ]. This presents a great opportunity for AI’s applications in education for further enhancing students’ learning outcomes, reducing teachers’ workloads [ 30 ] and making learning personalized [ 64 ], interactive and fun [ 50 , 53 ] for teachers and students.

With a growing number of AI powered ed-tech products in future, there will also be a lot of research on ethical AIEd. The risks of AI going wrong in education and the psychological impact this can have on learners and teachers is huge. Hence, more work needs to be done to ensure robust and safe AI products for all the stakeholders.

This can begin from the ed-tech companies sharing detailed guidelines for using AI powered ed-tech products, particularly specifying when not to rely on them. This includes the detailed documentation of the entire machine learning development pipeline with the assumptions made, data processing approaches used and the processes followed for selecting machine learning models. Regulators can play a very important role in ensuring that certain ethical principles are followed in developing these AI products or there are certain minimum performance thresholds that these products achieve [ 32 ].

6 Conclusion

AIEd promised a lot in its infancy around 3 decades back. However, there are still a number of AI breakthroughs required to see that kind of disruption in education at scale (including basic infrastructure). In the end, the goal of AIEd is not to promote AI, but to support education. In essence, there is only one way to evaluate the impact of AI in Education: through learning outcomes. AIEd for reducing teachers’ workload is a lot more impactful if the reduced workload enables teachers to focus on students’ learning, leading to better learning outcomes.

Cutting edge AI by researchers and companies around the world is not of much use if it is not helping the primary grade student in learning. This problem becomes extremely challenging because every learner is unique with different learning pathways. With the recent developments in AI, particularly reinforcement learning techniques, the future holds exciting possibilities of where AI will take education. For impactful AI in education, learners and teachers always need to be at the epicenter of AI development.

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Chaudhry, M.A., Kazim, E. Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021. AI Ethics 2 , 157–165 (2022). https://doi.org/10.1007/s43681-021-00074-z

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The Value of University Education Research Paper

Value of university education as to students’ impressions, research aim.

The research seeks to establish value of university education in the student’s context by considering impressions that can be attributed from it.

Research objective

The following objective is proposed by the researcher as aiding in achieving the above stated aim:

  • To study the students impressions of the value of university education.

Research Question

How does student’s impression inform the value of university education?

Literature Review

Students have various impressions on the value of university education. According to Tony and Neil (2011), these impressions pertain to a wide variety of aspects that relate to life of an individual. For instance, Holman (2009) affirms that university education is a panacea of an increased earning in life as he postulates that people with a first university degree earn more than the high school graduates while those individuals with graduate education earn more than their counterparts with bachelors degree. Therefore, students’ impression is that university education is seen as an economic value since future earnings of an individual is directly related to the level of education one has and since university is the highest institution of higher learning, those who attain university education are considered to have higher earnings than those without (Fonagy, 2001).

Moreover, according to Balzer (2008), it is believed that students have the impression that the university education has societal value in respect to the workforce. These higher learning institutions release to the society highly qualified human capital. For that matter, Fabbris (2007) holds that a workforce with university level education is more enhanced and more productive which again relates to higher output for the economy. In addition, Smart (2010) postulates that students believe that with their attainment of university education, they are better placed in driving economies of their states since they feel empowered in all aspects.

However, it is also believed that value of university education vary depending on the kind of institution attended by an individual. According to Bengelsdorf (2001), students believe that those who attend the elite institutions usually earn more than those who graduate from universities that are regarded to be of lower quality. Furthermore, Bligh, Ian and Harold (2003) affirms that students have impressions that quality of these institutions usually have noteworthy effect on the living standards of the graduates since they serve as weigh scales in the job market.

On the other hand, Kaplin and Barbara (2007) hold that students in most cases demand better value of their tuition fee at the university. For instance, most university students see value of the university education in terms of the time university lecturers spend with them and in terms of the grades they achieve from these classes. Moreover, students at the university level always value subjects that are covered by professors than those delivered by tutors since the former are seen as authority in their field of study (Leahy, 2003).

Research Methodology

This research takes the approach of a case study where by it is expected to catch impressions of students on the value of university education. Employing an exploratory, qualitative research methodology will be the best approach given its ability to identify the students’ impressions of the value of university education. Creswell’s (2003) affirms that concerning interviews and case studies, a qualitative approach offers the best option for examining these approaches. Qualitative research therefore involves collection of a variety of empirical materials (Newman & Benz, 1998).

Sampling and Data Collection Methods

The study population will be made of the university students of Cambridge University which will be the case study in this research. In respect to sampling, the research will adopt probability sampling among the students. Probability sampling will be used since it provide an excellent way of selecting representative samples from large and known population (Babbie, 2010:225).

To facilitate smooth data collection exercise, the researcher will obtain personal information of the students making up the study sample from the university registry. Their contact information will be obtained to facilitate communication of the researcher and the respondents (Holmes, 1993). Request letters will be prepared and delivered to the study subjects through post office. The researcher will also avail envelops that are ‘postage paid’ to facilitate response from the respondents. However, telephone calls will be used to make a follow up for those who fail to respond on time.

From the responses that will be obtained, a decision will therefore be made on those who will have accepted to participate in the research exercise and those who will have declined the request will be replaced. Those who will have accepted to participate will be assigned numerical numbers. A total of 10 members will then be randomly picked from the list to avoid biasness (Johnson & Christensen, 2010).

The study will employ interviews. These interviews will be in form of both open and closed ended format. Open ended interview questions will be used so as to capture as much data as possible (Creswell, 2003). Moreover, the interview process will assume structured format. To enhance participation, the researcher will give the respondent the opportunity to choose venues that are convenient to them (Maxfield & Babbie, 1995).

Ethical issues for the study

The first ethical consideration that the researcher will consider in conducting the research is to obey the cardinal rule of voluntary participation amongst participants. This ethical issue is supported by Reiss and Judd (2000) who affirm that when doing a research, participants should not be coerced into taking part in the study.

In addition, closely related to the cardinal rule of voluntary participation according to Bartlett, Kotrlik and Higgins (2001) is the prerequisite of informed consent. The researcher will ensure that his participants are informed of the procedure of the research and they will be at liberty to consent before being part of the study sample.

Proposed Interview Questions

  • What is the main value of university education to you?
  • What are some of the other values that are attributed to university education?
  • What is your level of university education?
  • Undergraduate
  • Postgraduate
  • Will you enrol for further studies after your current level?
  • Do you love university education?

Explanation and evaluation of the two significant questions

The two significant questions in this questionnaire is question number one ‘What is the main value of university education to you?’ and question number two ‘What are some of the other values that are attributed to university education?’ First and foremost, question one is designed to elicit some qualitative impressions of the core value of university education from students’ perspective. The question is meant to determine the main reason ascribed by students to be the pushing factor for their university education.

On the other hand, the second question is also designed to espouse other students’ impressions of the value of university education. As it can easily be understood there are several impressions that might be relating to the value of the university education apart from the first one mentioned in question one. Therefore, these other impressions by students will be captured in the second question. However, sampling and data collection methods will follow the defined sampling procedures as earlier discussed.

Since the study sample will be made of the university students of Cambridge University which will be the case study in this research, probability sampling method will be adopted since it gives equal opportunity to all entities in the population. This is designed to ensure that there is no biasness whatsoever and that the resultant sample is representative of the entire population

Data Analysis

A continuous method of data analysis was employed in analysis of the data. This ensured that there was no information that got lost in the analysis since information was recorded immediately (Birks, Chapman, & Francis, 2008) through note taking. Pertinent information was singled out which were subjected to interpretation using classification and coding techniques (Patton 2002)

In the analysis, there were several steps that were utilised. Firstly, there was scrutiny in checking whether the information provided is accurate. The researcher then analysed the information provided with the need of identifying important information. The useful information was then noted down in form of short notes. The researcher then transcribed short notes (Reiss & Judd, 2000).

Nonetheless, it is imperative to acknowledge that there was a high return rate of question that accounted for 100 percent of the total number of questionnaires that were dispatched to respondent. In respect to these answered questionnaires, out of ten, seven students were undertaking undergraduate while three students were in postgraduate level. This implies that the undergraduate population accounts for the largest portion of the university students which totals to 70 percent. On the other hand, the study results indicate that postgraduate students only accounts for thirty percent of the university population. Therefore, from these results, it can be affirmed that undergraduate students forms the largest population of the university community.

In addition, from the survey, the results indicated that most students were not willing to further their studies after their current level. For instance, sixty percent of the respondents were of the opinion that they will not enrol for further education after their current level while forty percent were willing to enrol for further studies after their current level. In connection to this, it can be opined that this is the reason why there are fewer people with higher degree qualification and again one of the reasons why higher earnings are also in few people which was ascribed as one of the values of university education. However, in relation to the last question of the questionnaire, it was evident from the respondents that most students love university education since eighty percent of the population were lovers of university education.

On the other hand, in respect to question one that pertains to qualitative study, most students associated their main value for education to improve their earnings. However, other values of the university education included improvement of ones status in the society, to be knowledgeable, to achieve self actualization among others.

Evaluation of the research

The research was conducted successfully since objective and the aim was accomplished. For that matter, the researcher was able to explore students’ impression of the value of university education of which the main value was established to be the desire to better their earnings. Nonetheless, the pilot study also had several lessons that future researchers can benefit from. For example, it was evident that the university students’ population have several similar traits that cut across the population. Therefore, any portion of the population selected for study is likely to generate results that can easily be generalised to the whole population.

Moreover, the research instrument that was adopted for this study was appropriate given nature of the study sample that was under investigation. The questionnaires were adopted and they were administered through mails since some of the respondents could not avail themselves easily. In addition, questionnaires were also adopted since the study sample involved educated people. Therefore, it was assumed by the researcher that they will easily read and understand the questions and provide appropriate answers. Moreover, this can be termed as successful since all questionnaires were filled correctly and the return rate was at 100 percent.

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Fabbris, L. (2007) Effectiveness of University Education in Italy: Employement, Competences, Human Capital. Italy: Verlag.

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Questionnaire

  • Chicago (A-D)
  • Chicago (N-B)

IvyPanda. (2022, May 7). The Value of University Education. https://ivypanda.com/essays/university-education-values/

"The Value of University Education." IvyPanda , 7 May 2022, ivypanda.com/essays/university-education-values/.

IvyPanda . (2022) 'The Value of University Education'. 7 May.

IvyPanda . 2022. "The Value of University Education." May 7, 2022. https://ivypanda.com/essays/university-education-values/.

1. IvyPanda . "The Value of University Education." May 7, 2022. https://ivypanda.com/essays/university-education-values/.

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  • 12 February 2024

China conducts first nationwide review of retractions and research misconduct

  • Smriti Mallapaty

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The reputation of Chinese science has been "adversely affected" by the number of retractions in recent years, according to a government notice. Credit: Qilai Shen/Bloomberg/Getty

Chinese universities are days away from the deadline to complete a nationwide audit of retracted research papers and probe of research misconduct. By 15 February, universities must submit to the government a comprehensive list of all academic articles retracted from English- and Chinese-language journals in the past three years. They need to clarify why the papers were retracted and investigate cases involving misconduct, according to a 20 November notice from the Ministry of Education’s Department of Science, Technology and Informatization.

The government launched the nationwide self-review in response to Hindawi, a London-based subsidiary of the publisher Wiley, retracting a large number of papers by Chinese authors. These retractions, along with those from other publishers, “have adversely affected our country’s academic reputation and academic environment”, the notice states.

A Nature analysis shows that last year, Hindawi issued more than 9,600 retractions, of which the vast majority — about 8,200 — had a co-author in China. Nearly 14,000 retraction notices, of which some three-quarters involved a Chinese co-author, were issued by all publishers in 2023.

This is “the first time we’ve seen such a national operation on retraction investigations”, says Xiaotian Chen, a library and information scientist at Bradley University in Peoria, Illinois, who has studied retractions and research misconduct in China. Previous investigations have largely been carried out on a case-by-case basis — but this time, all institutions have to conduct their investigations simultaneously, says Chen.

Tight deadline

The ministry’s notice set off a chain of alerts, cascading to individual university departments. Bulletins posted on university websites required researchers to submit their retractions by a range of dates, mostly in January — leaving time for universities to collate and present the data.

Although the alerts included lists of retractions that the ministry or the universities were aware of, they also called for unlisted retractions to be added.

research paper about university education

More than 10,000 research papers were retracted in 2023 — a new record

According to Nature ’s analysis, which includes only English-language journals, more than 17,000 retraction notices for papers published by Chinese co-authors have been issued since 1 January 2021, which is the start of the period of review specified in the notice. The analysis, an update of one conducted in December , used the Retraction Watch database, augmented with retraction notices collated from the Dimensions database, and involved assistance from Guillaume Cabanac, a computer scientist at the University of Toulouse in France. It is unclear whether the official lists contain the same number of retracted papers.

Regardless, the timing to submit the information will be tight, says Shu Fei, a bibliometrics scientist at Hangzhou Dianzi University in China. The ministry gave universities less than three months to complete their self-review — and this was cut shorter by the academic winter break, which typically starts in mid-January and concludes after the Chinese New Year, which fell this year on 10 February.

“The timing is not good,” he says. Shu expects that universities are most likely to submit only a preliminary report of their researchers’ retracted papers included on the official lists.

But Wang Fei, who studies research-integrity policy at Dalian University of Technology in China, says that because the ministry has set a deadline, universities will work hard to submit their findings on time.

Researchers with retracted papers will have to explain whether the retraction was owing to misconduct, such as image manipulation, or an honest mistake, such as authors identifying errors in their own work, says Chen: “In other words, they may have to defend themselves.” Universities then must investigate and penalize misconduct. If a researcher fails to declare their retracted paper and it is later uncovered, they will be punished, according to the ministry notice. The cost of not reporting is high, says Chen. “This is a very serious measure.”

It is not known what form punishment might take, but in 2021, China’s National Health Commission posted the results of its investigations into a batch of retracted papers. Punishments included salary cuts, withdrawal of bonuses, demotions and timed suspensions from applying for research grants and rewards.

The notice states explicitly that the first corresponding author of a paper is responsible for submitting the response. This requirement will largely address the problem of researchers shirking responsibility for collaborative work, says Li Tang, a science- and innovation-policy researcher at Fudan University in Shanghai, China. The notice also emphasizes due process, says Tang. Researchers alleged to have committed misconduct have a right to appeal during the investigation.

The notice is a good approach for addressing misconduct, says Wang. Previous efforts by the Chinese government have stopped at issuing new research-integrity guidelines that were poorly implemented, she says. And when government bodies did launch self-investigations of published literature, they were narrower in scope and lacked clear objectives. This time, the target is clear — retractions — and the scope is broad, involving the entire university research community, she says.

“Cultivating research integrity takes time, but China is on the right track,” says Tang.

It is not clear what the ministry will do with the flurry of submissions. Wang says that, because the retraction notices are already freely available, publicizing the collated lists and underlying reasons for retraction could be useful. She hopes that a similar review will be conducted every year “to put more pressure” on authors and universities to monitor research integrity.

What happens next will reveal how seriously the ministry regards research misconduct, says Shu. He suggests that, if the ministry does not take further action after the Chinese New Year, the notice could be an attempt to respond to the reputational damage caused by the mass retractions last year.

The ministry did not respond to Nature ’s questions about the misconduct investigation.

Chen says that, regardless of what the ministry does with the information, the reporting process itself will help to curb misconduct because it is “embarrassing to the people in the report”.

But it might primarily affect researchers publishing in English-language journals. Retraction notices in Chinese-language journals are rare.

Nature 626 , 700-701 (2024)

doi: https://doi.org/10.1038/d41586-024-00397-x

Data analysis by Richard Van Noorden.

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Applicants must also have completed  ECFS 200 Introduction to Early Childhood & Family Studies before starting the ECFS program. Please note:

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Gail E. Joseph

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    A. O. Moscardini. This is a conceptual paper that examines the origin and development of universities and their current role in global society. There has been an unprecedented and exponential growth of technology and artificial intelligence capabilities over the past ten years which is challenging current working practices and affecting all ...

  6. PDF Advantages and Disadvantages of Using e-Learning in University ...

    In university education, online learning is explained as learning that takes place completely or partially over the internet (Gilbert, 2015). Online learning is beneficial to a number of learners and appears as more common in settings from elementary schools to high schools and into post-secondary education.

  7. PDF Essays on Higher Education and Inequality

    Education Policy Research, including Meg Nipson, Jon Fullerton, Hong Yin, and Jesse Dalton, for facilitating data access and storage. I would like to thank the American Education Research Association for a dissertation fellowship and the Institute of Education Sciences (Grant R305B150012 to Harvard University)

  8. Research in Education: Sage Journals

    Research in Education provides a space for fully peer-reviewed, critical, trans-disciplinary, debates on theory, policy and practice in relation to Education. International in scope, we publish challenging, well-written and theoretically innovative contributions that question and explore the concept, practice and institution of Education as an object of study.

  9. Lifelong Learning in the Educational Setting: A Systematic ...

    This systematic literature review aimed to provide updated information on lifelong learning in educational research by examining theoretical documents and empirical papers from 2000 to 2022. This review sought to identify concepts, theories, and research trends and methods linked to lifelong learning in educational research in different countries. Our review findings showed that theoretical ...

  10. Study environment factors associated with retention in higher education

    study environment. institutional factors. teaching. Since the establishment of formal education, student dropout has been a major focus of both educational practice and research (Aljohani, 2016; Union, 2015) and the body of research concerned with student retention in higher education is extensive. There are many reasons for wishing to minimize ...

  11. PDF Students' Perceptions towards the Quality of Online Education: A

    861. Students' Perceptions towards the Quality of Online Education: A Qualitative Approach. Yi Yang Linda F. Cornelius Mississippi State University. Abstract. How to ensure the quality of online learning in institutions of higher education has been a growin g concern during the past several years.

  12. Assessing the Quality of Education Research Through Its Relevance to

    Federal education policies such as the No Child Left Behind Act (NCLB) and the Every Student Succeeds Act (ESSA) promote the use of evidence in education policymaking (Arce-Trigatti et al., 2018; Penuel et al., 2017; Wentworth et al., 2017).The federal government has also played an important role in funding knowledge utilization centers in the past decade with an emphasis on measuring research ...

  13. (PDF) Higher Education and Society: A research report

    Allan Cochrane The Open University (UK) Show all 10 authors Abstract and Figures This report draws on a substantial body of research undertaken by the Open University's Centre for Higher...

  14. The ideology of crisis in higher education

    Arguably, the marketisation crisis has resulted in a new sub-field of higher education research that has evolved out of this sense of crisis christened 'critical university studies' by Jeffrey Williams (Williams, 2012). The nature of the marketisation crisis and the neo-liberal principles which underscore it have been a target of campus ...

  15. Exploring the impact of artificial intelligence on teaching and

    This paper explores the phenomena of the emergence of the use of artificial intelligence in teaching and learning in higher education. It investigates educational implications of emerging technologies on the way students learn and how institutions teach and evolve. Recent technological advancements and the increasing speed of adopting new technologies in higher education are explored in order ...

  16. Harvard University Theses, Dissertations, and Prize Papers

    The Harvard University Archives' collection of theses, dissertations, and prize papers document the wide range of academic research undertaken by Harvard students over the course of the University's history.. Beyond their value as pieces of original research, these collections document the history of American higher education, chronicling both the growth of Harvard as a major research ...

  17. Artificial Intelligence in Education (AIEd): a high-level academic and

    In the past few decades, technology has completely transformed the world around us. Indeed, experts believe that the next big digital transformation in how we live, communicate, work, trade and learn will be driven by Artificial Intelligence (AI) [83]. This paper presents a high-level industrial and academic overview of AI in Education (AIEd). It presents the focus of latest research in AIEd ...

  18. Future in Educational Research

    Future in Educational Research (FER) focuses on new trends, theories, methods, and policies in the field of education. We're a double-blind peer-reviewed journal. Our original articles advance empirical, theoretical, and methodological understanding of education and learning. We deliver high quality research from developed and emerging regions ...

  19. International Journal of Educational Research

    The International Journal of Educational Research publishes research papers in the field of Education. Papers published in IJER address themes of major interest to researchers, practitioners, and policy makers working in different international contexts.

  20. PDF Education for Sustainability: Quality Education Is A Necessity in

    The research is financed by Botho University (Sponsoring information) ... This paper seeks to discover if quality in education affects the employability of the graduates. Furthermore it seeks to unveil the quality of education which Botho university and other tertiary institutions in Francistown offer, and if there are shortcomings, suggest ...

  21. The problem with international students' 'experiences' and the promise

    This article considers the value and implications of taking the notion of experience as a conceptual starting point for debates about international students in higher education. Within this field of research, foregrounding international students' experiences has often been used to assess the quality, impacts and possibilities of studying abroad ...

  22. AI technologies for education: Recent research & future directions

    Artificial intelligence in education (AIEd) research has been conducted in many countries around the world. ... 205 university students - 112 were undergraduates in Dubai, UAE and 93 were undergraduates in Barcelona, Spain ... A categorical and bibliometric meta-tend analysis of every research paper published in the journal, pain. Pain, 142 ...

  23. The Value of University Education Research Paper

    Yes. This research paper, "The Value of University Education" is published exclusively on IvyPanda's free essay examples database. You can use it for research and reference purposes to write your own paper. However, you must cite it accordingly . Donate a paper.

  24. China conducts first nationwide review of retractions and research

    Chinese universities are days away from the deadline to complete a nationwide audit of retracted research papers and probe of research misconduct. By 15 February, universities must submit to the ...

  25. Education Studies

    The Education Research and Policy option prepares you to apply to the following COE graduate programs: Education, Policy, Organizations, and Leadership (M.Ed.) ... University of Washington College of Education • 2012 Skagit Lane, Miller Hall • Box 353600 • Seattle, WA 98195-3600

  26. Latest articles from Research Papers in Education

    New and old educational inequalities in socio-cultural minorities: exploring the school choice experiences of families under the new school admission system in Chile. Juan de Dios Oyarzún, Lluís Parcerisa & Alejandro Carrasco Rozas. Published online: 14 Mar 2023. 158 Views.

  27. CEHD Presentations at AERA

    The CEHD is excited to welcome the 2024 AERA annual meeting in PHILADELPHIA, APRIL 11-14, 2024 Learn · Engage · Inspire. Each year, the American Educational Research Association Annual Meeting is the world's largest gathering of education researchers and a showcase for groundbreaking, innovative studies in an array of areas.

  28. Professors proceed with caution using AI-detection tools

    The group's second working paper, delving further into AI detection and the usage of tools, is slated for completion this spring. Elizabeth Steere, a lecturer in English at the University of North Georgia, has written about the efficacy of AI detectors. She and other UNG faculty members use the AI detector iThenticate from Turnitin.

  29. Early Childhood & Family Studies (ECFS)

    ECFS supports the application of theory and research into practice by: ... Applicants need to write and submit three essays. Each essay should be 250 words or fewer. Use the following three prompts for your essays: ... University of Washington College of Education • 2012 Skagit Lane, Miller Hall • Box 353600 • Seattle, WA 98195-3600