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Maximising the impact of teaching assistants to better support students

Nationally and internationally, extensive research from IOE is changing how teaching assistants are deployed in schools and informing new policies and practices in education systems.

12 April 2022

The number of teaching assistants (TAs) working in mainstream schools has more than trebled in the past 20 years. In England, TAs comprise almost a third of the school workforce. Provision for children with Special Educational Needs and Disabilities (SEND) relies heavily on TA support, and in some cases, this can lead to separation and segregation in the classroom rather than inclusion.  

Improving TA support across classrooms  

An extensive programme of research at IOE, UCL’s Faculty of Education and Society, led by Professor Peter Blatchford and Drs Rob Webster and Paula Bosanquet has transformed how TAs are deployed in schools to maximise their impact and reduce stigmatisation of lower attaining pupils and those with SEND.

Their landmark study of pupil progress - ‘Deployment and Impact of Support Staff’ (DISS) - identified an urgent need for schools to reassess and change the way they use TAs, and the ‘Effective Deployment of Teaching Assistants’ project, undertaken in collaboration with schools, developed guidance rethinking the TA role.

This led to a series of handbooks for schools and TAs, underpinned by the research findings which formed the basis of the ‘Maximising the Impact of Teaching Assistants’ handbook and national guidance written with Professor Jonathan Sharples at the Education Endowment Foundation (‘Making Best Use of Teaching Assistants’ - MBUTA).    The suite of freely accessible resources allows schools to ensure TAs are deployed to support better pupil outcomes. Government data shows 80 per cent of headteachers have read the MBUTA guidance and nine out of ten headteachers surveyed in schools with the highest proportions of disadvantaged pupils said IOE guidance has influenced their school practice.

Schools now better recognise that the classroom role of TAs should be broadened and that they should not work exclusively with lower attaining pupils and those with SEND. To inform policy-making, the team built relationships with key decision-makers, including senior officials in local authorities, the Department for Education (DfE) and Ofsted.  

Influencing the national conversation  

The MITA research and MBUTA recommendations have informed national policy documents and debates, including a debate in the House of Commons on the use of TAs in English schools and in the DfE’s post-COVID guidance for schools.

Over 150,000 pupils and 780 schools have benefitted from the IOE-led programme of training and professional development across Yorkshire and Lincolnshire based on the research.

The Institute for Fiscal Studies reported that this training improved English outcomes at Key Stage 2, equivalent to three weeks of additional teaching. Teachers and TAs also benefited with increased confidence, better pedagogic understanding and a clearer sense of purpose.  

An international approach  

This important research has also had an international impact. In New Zealand, the government used the MBUTA guidance to inform national policy on paraprofessionals and training for more than 3,500 teachers, with a demonstrable impact on practice.     The OECD has recognised the international impact of the research, and MITA also won the Public Engagement & Impact Team Award in 2019, presented by the British Educational Research Association.  

Research synopsis

Maximising the Impact of Teaching Assistants (MITA) 

Extensive research from IOE is maximising the impact of Teaching Assistants (TAs) in schools nationally and internationally. The MITA programme has changed the way TAs are deployed in schools and has informed policies and practice in education systems, regionally, nationally and internationally. 

• Professor Peter Blatchford's academic profile
• Professor Jonathan Sharples' academic profile
• Dr Rob Webster's academic profile
• Dr Paula Bosanquet's academic profile
• Research Brief: Deployment and Impact of Support Staff project 
• IOE, UCL’s Faculty of Education and Society 
• IOE REF 2021
• Image credit: Maximising TAs

• Research article
• Open access
• Published: 15 November 2022

The impact of a virtual teaching assistant (chatbot) on students' learning in Ghanaian higher education

• Harry Barton Essel 1 ,
• Dimitrios Vlachopoulos   ORCID: orcid.org/0000-0002-2033-7343 2 ,
• Akosua Tachie-Menson 1 ,
• Esi Eduafua Johnson 1 &
• Papa Kwame Baah 1  

International Journal of Educational Technology in Higher Education volume  19 , Article number:  57 ( 2022 ) Cite this article

54 Citations

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Chatbot usage is evolving rapidly in various fields, including higher education. The present study’s purpose is to discuss the effect of a virtual teaching assistant (chatbot) that automatically responds to a student’s question. A pretest–posttest design was implemented, with the 68 participating undergraduate students being randomly allocated to scenarios representing a 2 × 2 design (experimental and control cohorts). Data was garnered utilizing an academic achievement test and focus groups, which allowed more in depth analysis of the students’ experience with the chatbot. The results of the study demonstrated that the students who interacted with the chatbot performed better academically comparing to those who interacted with the course instructor. Besides, the focus group data garnered from the experimental cohort illustrated that they were confident about the chatbot’s integration into the course. The present study essentially focused on the learning of the experimental cohort and their view regarding interaction with the chatbot. This study contributes the emerging artificial intelligence (AI) chatbot literature to improve student academic performance. To our knowledge, this is the first study in Ghana to integrate a chatbot to engage undergraduate students. This study provides critical information on the use and development of virtual teaching assistants using a zero-coding technique, which is the most suitable approach for organizations with limited financial and human resources.

Introduction

There has been a rapid increase in chatbot usage in various fields in recent years. Notably, one essential field where chatbots and virtual assistants are increasingly employed is education (Clarizia et al., 2018 ). According to Agarwal et al. ( 2022 ) chatbots are software applications, which are able recognize patterns from inputs and produce outcomes as per the input. The chatbots are called virtual assistants, when they are designed to understand the needs of their users, through artificial intelligence (AI) methods, and reply back to them in natural language. In education, there has been a considerable growth in the representation of chatbots whose major goal is to develop knowledge for individual students, usually on a certain topic. The main goal of these chatbots is to develop new knowledge like a human teacher (Han & Lee, 2022 ; Pérez et al., 2020 ). Chatbots are now used as virtual assistants or agents to enhance learning and teaching. The increased use of chatbots is attributable to current advances in Natural Language Processing (Adamopoulou & Moussiades, 2020 ). More readily available computing power and communication technologies have facilitated the rapid development and deployment of chatbots (Maroengsit et al., 2019 ) in education. Besides, chatbots can help higher education institutions improve their current services, cut staff expenses, and develop innovative services (Hien et al., 2018 ). Pérez et al. ( 2020 ) identify two types of educational chatbots: Service‐oriented chatbots and Teacher‐oriented chatbots. Service‐oriented chatbots offer support for student queries during enrollment and admissions, and library services. Teacher-oriented chatbots act like classroom assistants (Chou et al., 2021) to generate knowledge, increase student engagement and provide smart feedback (Khan, 2019 ; Vázquez-Cano et al., 2021 ). Several chatbot development platforms produce education bots that engage students and contribute brief but relevant knowledge (Kumar, 2021 ). The development of some chatbots may require complex computational skills, while others may require zero coding techniques. Flow XO, DialogFlow, and Botsify are examples of chatbots that anyone, not necessarily tech-savvy, can consider when aiming to implement such solutions in their organizations (Satam et al., 2020 ). With the advancement of bot platform features, the user interface of the platform becomes very simple and intuitive to enable educational institutions, with limited software development and human resource capacity to create the bots by themselves.

Current research highlights that chatbots may address the challenge of insufficient student–instructor interaction (Clarizia et al., 2018 ). Especially in contexts like the Ghanaian higher education institutions, where the instructor–student ratio is high (Essel et al., 2019 ; Tsyhaniuk & Akenten, 2021 ), the use of a chatbot may provide automatic and instantaneous responses to students’ queries similar to chatting with a course instructor. This leads to reduced workload for the instructor and more engaging learning experience for the students. In this study we implemented a zero-coding chatbot, named KNUSTbot, in a multimedia programming course at Kwame Nkrumah University of Science and Technology (KNUST), which allows students to learn and reflect profoundly about multimedia programming via interaction. The effectiveness of student interaction with the KNUSTbot during the learning process was examined. The chatbot in the present study is a teaching assistant chatbot developed to accomplish a set of learning objects by determining intents and entities from a free text communication of a student leveraging Natural Language Processing without confining the student with a set of available options. This method allows for a more natural way of engaging (Yin et al., 2021 ).

The study’s Research Question (RQ) is the following:

How does the KNUSTbot, used as an intermediary between students and instructors, affect the student’s learning in a multimedia programming undergraduate course?

The following are the study’s sub-research questions:

Is there a significant difference in academic results between students who interact with a chatbot and students interacting with a course instructor?

What are the perceptions of the experimental cohort on the chatbot as a means to facilitate their learning?

Chatbots in teaching and learning

Chatbots combine artificial intelligence (AI) and Natural Language Processing to interact with a human interlocutor at a certain level of conversation via text or voice (Pérez et al., 2020 ; Smutny & Schreiberova, 2020 ). Clarizia et al. ( 2018 ) describe chatbots as virtual assistants capable of answering questions and providing appropriate responses. Other authors adopted a text-based chatbot, which typically responds to questions by following a built-in rule set, allowing them to respond to their users (Budiu, 2018 ; Salas-Pico & Yang, 2022 ; Topal et al., 2021 ). AI relates to systems or machines that mimic human intelligence and self-alter based on accumulated data (Angelov et al., 2021 ). Chatbots are an example of software applications (Salas-Pico & Yang, 2022 ; Topal et al., 2021 ) that understand questions faster and provide efficient answers (Angelov et al., 2021 ). Examples of chatbots include FAQ chatbot (Han & Lee, 2022 ; Ranoliya et al., 2017 ), ELIZA, an early Natural Language Processing computer program that simulated the communication between humans and machines (Natale, 2019 ), the colMOOC, an conversational virtual agent that promotes learners' interaction within MOOC platforms (Tegos et al., 2019 ), the StudBot, which is an academic information systems chatbot (Vijayakumar et al., 2019 ) and the artificially intelligent course teacher chatbots, like Sammy (Gupta et al., 2019 ), which is closer to what we are experimenting in our study. These chatbots have received significant recognition in the educational ecosystem in diverse learning contexts.

Okonkwo and Ade-Ibijola ( 2020 ) reported that most chatbots employed in higher education are teacher-oriented chatbots. Mendoza et al. ( 2020 ) highlighted positive impressions from a sample of students when they were engaged with a chatbot. Studies have also reported that students employ chatbots to ask questions, receive responses, and receive individualized support (Hiremath et al., 2018 ; Mikic-Fonte et al., 2018 ; Pham et al., 2018 ; Sinha et al., 2020 ).

According to Yin et al. ( 2021 ), no significant difference in the learning achievements of undergraduate students randomized into experimental and control groups (without or with the support of a chatbot) were found; however, the research reported higher levels of motivation for the learners interacting with the chatbot. Arruda et al. ( 2019 ) designed a chatbot for computer science students, employed for goal-oriented requirements modelling; the students found the chatbot functional and desired to use it in the future. A study performed by Kamita et al. ( 2019 ) using chatbots and web courses to improve students’ mental health reported a higher probability of efficacy as chatbots guided self-learning, enhanced motivation, and lessened stress. The University of Georgia designed a chatbot named ‘Jill Watson’ adopted in a computer science course. Participating students were more responsive and they stated that they wanted to use this chatbot in different lessons (Lipko, 2016 ).

Students’ ability to interact with instructors, by asking questions, is an essential process of learning that can contribute to enhanced academic performance (Harper et al., 2003 ; Sandu & Guide, 2019 ; Vlachopoulos & Makri, 2021 ). University students in Ghana have inadequate interaction with their course instructors during class sessions. This issue is due to the increase in the student–instructor ratio (Essel et al., 2019 ), reducing time instructors spend with their students. Furthermore, studies have shown that students are hesitant to ask questions because they are constantly scared of the teacher’s negative feedback (Oktaria & Soemantri, 2021 ; Verleger & Pembridge, 2018 ). In mitigating these issues, some instructors engage students after classroom didactics, with instant messengers (e.g. WhatsApp) and social media platforms (e.g. Facebook messenger) to provide personalized assistance to the students. However, the challenge is the instructor not having enough time to respond to questions and provide timely and individualized feedback to students. The unspontaneous student–instructor interaction leads to shortcomings in student’s knowledge. Late response to a student’s question is a significant concern as students continuously strive for precise and prompt responses (Farhan et al., 2012 ). In this context, chatbots become relevant in situations where course instructors cannot provide adequate response for students’ learning at any time of the day (Yang & Evans, 2019 ). A chatbot can simulate human-like dialogue-based interactive communications to assist students in revisiting learning resources (Göschlberger & Brandstetter, 2019 ; Jomah et al., 2016 ; Smith & Evans, 2018 ), promoting learning achievement and self-efficacy (Chang et al., 2021 ) and enhancing adaptive learning (Fadhil & Villafiorita, 2017 ).

Furthermore, chatbots can assist in overcoming this difficulty by initiating conversations based on the student’s context, making students seem individually addressed (Hien et al., 2018 ; Howlett, 2017 ). A chatbot can be an intermediary between a student and an instructor, which allows students to concurrently control their learning and improvement at their pace without constraining them (Wang et al., 2021 ). Also, chatbots tend to stimulate questions from students who may be restrained from engaging in a conventional learning space (Verleger & Pembridge, 2018 ).

We employed a quasi-experimental pretest-posttest design in combination with a qualitative research method (focus groups), to collected more in-depth information on the students’ experience with the chatbot. The 68 participating undergraduate students were in their final year in a multimedia programming course offered by the Department of Publishing Studies at Kwame University of Science and Technology (KUNST). The experiment was conducted in the second semester of the 2021 academic year between April and August (16 weeks). Stratified sampling was chosen to create two groups with representative sample from the student population (Delice, 2010 ), which was divided into relatively similar subpopulations (strata) in terms of age, gender and academic performance. As part of the study, we garnered data on their socio demographics, including gender, age, cumulative weighted average grade, preferred instant messaging app, and previous experiences with AI chatbots.

The intervention procedure

We used the end-of-semester course achievement tests to measure the academic performance of the experimental and control groups. The achievement test had two sections. Section  1 contained different types of objective questions (multiple choice with single and double selection, matching, ranked order, and short answer). Section  2 of the test was a computer-based practical examination where students scripted a front-end website using HTML (Hypertext Markup Language) and CSS (Cascading Style Sheet). The lead investigator developed the test items. There were 40 objective-typed items in section A, and each item carried 1 point, and section B carried 60 points. The total maximum score for the test is 100 points. The reliability coefficient of the items was = 0.82, with the average difficulty level estimated at p  = 0.45. According to Hasançebi et al. ( 2020 ), values ranging between 0.30 and 0.49 suggest average item difficulty. The number of correct responses provided by students was used to determine their achievement. The total time estimate for the achievement test was 60 min (20 min for section A and 40 min for section B). The investigators developed the table of specifications based on the university’s standards to measure the content and construct validity of the test.

In addition, the investigators designed group interview (focus group) guide to collect comprehensive data about the students’ experience with the chatbot. The guide was given to the experimental group to estimate their observations of the chatbot. The focus group discourse was done in a single sitting, with physical presence and one week after the experiment and every student in the experimental group partook. To give more opportunities for interaction to all students, three focus groups were conducted. The focus groups aimed at interpreting the students' positive and negative encounters with the chatbot (KNUSTbot) for course interaction and their predisposition to interact with the KNUSTbot in future academic endeavors.

The KNUSTbot was developed using WhatsApp instant messaging app and Flow program. FlowXO is a Natural Language Processing platform that can build human-like conversational AI with state-of-the-art virtual agents in multiple languages and platforms (Flow, 2020 ). Two experienced multimedia programming experts and an instructional designer verified the KNUSTbot. The chatbot’s intents and entities comprise a data repository of standard learning content queries (HTML and CSS) built based on chats database continuously accumulated in a Learning Management System over eight years by the lead investigator. Current studies (Snodgrass Rangel et al., 2017 ) highlight that student data obtained by teaching staff during teaching and learning process has the potential to improve education, as well as the way educational institutions work. We understand that some types of research using quantitative, qualitative or mixed methods, may require prior contact and dialogue with individuals or communities as a normal and integral component to establish the design of the research and understand its value for the society and the discipline in question. These activities don’t require ethics review and approval (Government of Canada.-Panel on Research Ethics, 2018 ). We considered that the accumulation of student queries prior to the beginning of the “approved” study fall in this category, since the instructor didn’t invite the students for this interaction and simply used the published queries within the Learning Management System (LMS) of the university. In addition, these queries are not linked to any personal information, such as name, email, group, year of studies, etc. Finally, all students gave their consent to participate in the LMS, where this information is published.

The term “intent” describes the question a student requested, to which the chatbot is supposed to answer. At the same time, the entity represents a trigger connected to the intent to render a distinct and individualized context for the intent. In all, 1000 intents and entities were used as a dataset to train the bot. We included additional 70 intents (short text expressions, e.g. ‘Hi super programmer’, ‘Good job’, ‘I’m always at your service’) to engage and motivate the students during the interaction. Besides, the bot was trained with internet sources (e.g. Websites and YouTube videos).

Whatsapp was selected because of its user-friendliness and popularity among undergraduate students (Afful & Akrong, 2019 ). The students interacted with the chatbot using natural language. The interface of the KNUSTbot and how a student can interact with it is illustrated in Fig.  1 .

The interface of the KNUSTbot

A digital literacy test was administered to the students to ascertain their prior abilities on basic computer skills, the internet and the web, productivity programs, computer security and privacy, and digital lifestyle. This activity occurred in week one of the course and constituted the pre-test score. All 68 students were exposed to multimedia programming with HTML and CSS through face-to-face instruction from the second week to the sixteenth week. The course instructor demonstrated how to develop a front-end application using the scripting languages in a practical session. During the sessions, the instructor created two WhatsApp platforms for the control and the experimental cohort. The students were instructed to direct content-related questions to the WhatsApp platform. The experimental cohort was assigned to the WhatsApp group via the platform integrated with the chatbot. They engaged the chatbot at any time after a topic was taught; however, the instructor did not intervene in the experimental cohort’s interactions. No additional communication channels or contact points were available for the two groups. The control cohort interacted on the platform without the chatbot. The two cohorts were assessed with a post-learning achievement test at the end of the sixteenth week.

After the experiment with the use of the chatbot, focus groups were conducted by the lead investigator, who is an experienced scholar in educational research. Students in each group were initially asked similar questions designed to explore their perceptions of the educational experience they had with the chatbot and to probe for the positive and adverse viewpoints they highlighted from their experience. Another question honed in on whether students felt they have derived similar benefits from their interaction with the chatbot compared to the interaction they were used to have with the instructor. The last question of the focus group was related to whether students consider the benefits of interaction with the chatbot significant, so it can be implemented in other courses of their program of studies. Each focus group lasted approximately 45 min. Figure  2 depicts the intervention procedure.

Data analysis and ethical considerations

The Jamovi 2.0.0 package (Jamovi project, 2021 ) was used to analyze the data using descriptive and predictive statistics. The Shapiro–Wilk test (p > 0.05) indicated that the datasets were normally distributed. As a result, we obtained skewness and kurtosis values of ± 1.96 for the datasets, validating the normality of distribution (Essel et al., 2021 ). In the present study, the significance level for statistical tests was estimated at a p-value of less than p < 0.05. Frequency, percentage, Mean, and standard deviation were calculated for the students' sociodemographics. A Split-Plot Analysis of Variance (SPANOVA) was performed to assess the influence of the chatbot and instructor on students' Achievement Test scores over two-time sessions (pre-test and post-test). SPANOVA and independent-samples T-test were used to examine RQ1. All assumptions, including Homogeneity of variance (Levene’s: p pretest  = 0.08; P posttest  =  0.56 ), and normality of distribution (Shapiro–Wilk: p pretest  = 0.06; P posttest  =  0.27 ) were met.

To investigate RQ2, the investigators used the data from the focus groups, which took place at the University premises. The students were interviewed in groups of 11 or 12 people to create the dynamic of a conversation and to make the student feel more at ease (Witsenboer et al., 2022 ). The data were digitally recorded, transcribed, and manually coded under themes using content analysis. To create a theme, the content was coded in two levels. First level of coding included labels assigned to specifics fragments of the focus group, which could help us answer the RQ2. Following Witsenboer et al. ( 2022 ) it was also checked whether existing labels could be assigned to overlapping content. In the second level of coding we narrowed the focus to relatively fewer codes, directly related to RQ2.

The present study was conducted under the Helsinki Declaration (1975) and comparable ethical standards, with approval by the Humanities and Social Sciences Research Ethics Committee (HuSSRECC) of KUNST with number 233/22-08/2021. A written and verbal informed consent was solicited from the students.

Participants’ profile and traits

This study included 68 final year undergraduate students, 34 of whom were in the experimental group and the other 34 in the control group. All the students had the WhatsApp app installed on their phones. Regarding students' experience with chatbots, all 68 reported that they have encountered chatbots in their online activities outside their studies, with the 79% (54 students) encountering services-oriented chatbots. Table 1 illustrates the profile of the students and some of key traits for the interpretation of the findings. The median split categorized overall academic performance measured with Cumulative Weighted Average (CWA, Median = 65.71) and the years of experience (Median = 5.0) with the use of WhatsApp app.

Engagement with the chatbot

Figure 3 illustrates the preferred time during the day students engage with the chatbot, as well as the amount of interactions (number of chats) per hour. It is obvious that the students interact with the KNUSTbot mostly between 20.00 and 22.00, which is when the instructors are not usually available and responsive.

Students’ engagement (number of chats per hour) with the KNUSTbot

Figure  4 illustrates the amount of students, who interacted with the chatbot during the course. It is obvious that there were more students engaging at the beginning of the course, when they needed more information to understand how the course works and towards the end of the course, when they were preparing for their final assessment.

Number of students using the chatbot during the course

Figure  5 illustrates the number of student queries using the chatbot during the course. Again, it is obvious that there was more need for interaction during the last month of the course, when students were preparing for their final assessment.

Daily queries by students

Academic achievement scores

As shown in Table 2 , students in the experimental cohort received an average score of 40.6 (4.95) for course achievement before and 81.1 (3.19) after the experiment. Similarly, students in the control group received mean scores of 43.4 (4.09) and 65.2 (3.73) respectively. The results demonstrate that course achievement improved between the experimental group of students, who interacted on a real-time basis with the chatbot, and students who interacted with the instructor in real-time. Interaction between time and method of interaction, F(1, 66) = 87.5, p < 0.05, squared partial eta (η p 2 ) = 0.57 was found significant.

Using Cohen's guidelines using effect size: 0.01 = small, 0.06 = medium, 0.14 = big, these results suggested a very large effect size for interaction. In terms of real-time interactions with course instructors ( p < 0.001) and real-time chatbot interactions, there were significant differences between the posttest and pretest scores (p < 0.001) and no significant difference in cohorts before intervention in two groups (p > 0.05), the difference between cohorts following intervention was significant (p < 0.001). The results are illustrated in Tables 3 and 4 .

Sociodemographic variables for experimental cohort regarding course achievement

The investigators also wanted to observe after the experiment whether there was a significant difference in course achievement regarding sociodemographics. The results in Tables 5 and 6 illustrate that there was no statistically significant variation in gender ( p  > 0.05) and course achievement, as well as age ( p  > 0.05) and course achievement.

In addition, the results show no statistically significant difference between prior academic performance and the posttest score ( p  = 0.51), as well as WhatsApp user experience and the posttest score ( p  = 0.34).

Findings from the focus group

Focus group discussions (after posttest) were conducted with the experimental cohort to garner data on the perceptions regarding their interaction with the KNUSTbot. Students were asked to appraise the positive and adverse viewpoints of their interaction with the KNUSTbot during this discussion. Table 7 comprises the thematic codes based on the results from the discussions. Student appraisals of the discourse were utilized to generate the thematic codes.

Most students were satisfied with their interactions with the KNUSTbot during the course, as exposed in Table 7 . Below there are some representative examples of student statements confirming the positive viewpoints.

The AI chatbot is easy to use

“I never imagined that a chatbot could be so easy to use through my mobile phone” “It was very easy to share the responses from the chatbot with other students through WhatsApp. This is the app we use to communicate as well, so it was useful” “It allowed me to study the course with ease”

Searching for and evaluating information

“I also got links to websites that provided access to video and text tutorials in relation to the course content” “I didn’t only receive an answer to my question, but also sources to consult to better understand the chatbot’s answer”

More self-belief to learn more effectively

“I realized that I can find the answers on my own” “At the beginning I was insecure with the information I received by soon I was able to confirm the answer on my own”

Giving immediate feedback

“I realized that the response to my questions was very swift” “I was able to get 24/7 quick answers to my questions when learning HTML and CSS which I have never encountered in any other course”

Overall, the students were satisfied with the instantaneous and immediate responses they received to inquiries during chats. They were generally welcoming the usage of the chatbot as a learning tool. They didn’t encounter delayed responses to their questions asked within the chat platform compared to their experiences with the instructor, where their questions may have received a delayed response or no answer at all. Despite this positive feedback, student highlighted some negative viewpoints on the integration of the chatbot in their learning, which can be summarized in the statements below:

Concerns about responses being outdated/not relevant

“My experience with the chatbot was not pleasant. I realized I was getting similar answers to the variety of questions I asked during the chat sessions” “Sometimes it was frustrating asking for one thing and getting an answer about a different topic” “The majority of the information I had seemed outdated to me. Besides, links/URL provided were broken, and this situation worried me at times”

Inability to think in depth

“I missed more in-depth answers from the chatbot and not simply definitions and links to find additional resources. I feel only the instructor can do this” “Often I needed more explanation or even justification of the answer I received from the chatbot. I wasn’t able to understand what to do with the given information” “I wished the course instructor supported the AI chatbot at a point in time”

There is a dearth of detailed interactions

“Few times I just received a link or a short answer to my question” “I wish there were more follow-up answers since not always I understood the information I got from the chatbot” “Some of my questions couldn’t be answered because they were about how to apply the knowledge from the course. I needed more instructions”

After synthesizing the negative feedback received, we can say that the students complained about their interaction with the chatbot because it didn’t carry out in-depth learning and the human element was missing. They also acknowledged that the chatbot gave the same responses to different questions. The need for more instructions on how to apply the knowledge acquired from the course was also highlighted.

Notwithstanding, students formed good views after interacting with the chatbot and fully appreciated the interaction approach. The vast majority recommended the integration of chatbots in other courses of their studies and more than half of the participants preferred the chatbot comparing to the interaction with the instructor. Some representative responses are presented below:

“I felt really elated interacting with the chatbot in this course. I had responses to all the questions I asked. At one point, I felt like I was chatting with the instructor. It is simple interacting with the chatbot” “Interacting with the chatbot is a great way to learn more about HTML and CSS. I expect that chatbot learning will be used in more tailored educational systems in the future” “The integration of the chatbot into the teaching and learning of multimedia was engaging, motivating and exciting for me, as it was a new experience and I felt more sense of belonging in the course” “I was surprised by the kind of feedback I received during my conversation with the chatbot. It supplied me with a lot of reference links”

After analyzing the results from the focus groups, it is reassuring that, given the right conditions, students might appreciate the integration of chatbots as part of a course since it simulates and assists them in learning abstract concepts in-depth. This positive response from the students suggests the possibility of increasing the limited interaction sessions between academic staff and students, ensuring self-regulated learning and experiencing novel learning cultures, where academic staff is assisted by integrated emerging technologies into their courses. As a whole, engaging with the chatbot can support students in connecting what they are learning with real-world challenges or precedents, encouraging them to reason in-depth regarding what they are studying.

The study indicated that students in the experimental cohort who engaged with the chatbot performed better than students in the control cohort who interacted with the course instructor. The use of chatbots can be a significant progression and innovation for heightening challenging subject learning (Clarizia et al., 2018 ; Okonkwo & Ade-Ibijola, 2020 ) such as multimedia programming.

The findings indicate no significant difference related to gender, age, experience with Whatsapp, academic performance, and the post-test scores of the experimental cohort. This confirms Sandu and Gide ( 2019 ), who also reported no significant difference between gender and age and the adoption of a chatbot. Regarding WhatsApp, our findings demonstrate that years of experience in using it didn’t affect the students’ post-test score. This is probably because it is a very popular and intuitive app widely used in Ghanaian higher education (Boateng & Tindi, 2022 ). There was a significant difference between daily Whatsapp use and post-test scores of the experimental cohort. The possible reason for this finding can be attributed to the immediacy of the feedback provided by the chatbot reflecting the improvement of learning, while the instructor usually delayed more to answer due to the timing of the questions were sent (since 2 most questions were sent outside office hours) and the big student-instructor ratio (Essel et al., 2019 ), which doesn’t allow instructor to spend enough time with their students and provide them with timely responses.

The quantitative analysis demonstrates that engaging students with a teaching assistant chatbots positively impacts academic performance. The qualitative analysis provided evidence of students’ satisfaction with the use of the chatbot, which can be attributed to the instantaneous feedback they received from the chatbot, as well as the enormous contribution to the learning process via having more engagement with chatbot at different times, and without encountering any delays in the interaction process. One of the purposes of using AI-powered teaching assistant chatbots, according to Chang et al. ( 2021 ) and Sandu and Gide ( 2019 ), is to deliver timely knowledge to specific students to surmount difficulties that arise during the learning process. Besides, the comments from the experimental cohort suggest that the student gained understanding and confidence to complete the course which translated in their improved academic performance. They also found learning to be interesting and interactive as their engagement with the chatbot enhanced the organization and re-examination of knowledge acquired. This outcome is consistent with Chang et al. ( 2021 ) finding that students' awareness arose due to the possibility to grasp and perform in-depth thinking by studying pertinent information.

Conclusions, limitations and implications of the study

The present study's main findings and arguments support the value of using chatbots in higher education, since the students who interacted with the chatbot performed better than students in the control cohort who interacted with the course instructor. This is especially relevant for countries like Ghana, where student–teacher ratio is high and the provision of timely response and feedback to students is a challenge. At the same time, students were very satisfied with the use of the chatbot, mainly because it provided them with instantaneous feedback at different times, without encountering any delays in the interaction process.

However, specific difficulties associated with the AI-powered teaching assistant must be overcome to use this approach effectively. Academic staff must have access and knowledge to customize and integrate chatbots to assist students learning. Since students may have encountered other chatbots such as the service-oriented chatbots, it is also advisable to make the transition to the use of teaching assistant chatbot simpler. Furthermore, it is encouraged that different motivators should be used to urge students to engage the chatbot. For example, the integration of gamification to enhance students’ engagement and interaction with chatbot can be a reinforcer, in line with the recommendations of Fadhil and Villafiorita ( 2017 ). Moreover, a micro-learning approach is a viable strategy for integrating teaching assistant chatbots in the educational setting (Yin et al., 2021 ). Other instant messaging platforms (Telegram or WeChat) can be employed to encourage students' interactions with teaching assistant chatbots due to their familiarity with these platforms (Boateng & Tindi, 2022 ).

Though this study engaged students with a chatbot developed with zero coding and in one course, the results are encouraging for the use of a teaching assistant chatbot in similar contexts. Specifically, within an institution/country with very limited resources (human and technological), the fact that we were able to execute such innovation successfully, with positive impact on academic performance and student satisfaction, make us confident that it can enact positive change in the teaching and learning process.

In this context, it is important to mention that the present study has also some limitations. It consists entirely of fourth-year students in a single university department, so future studies may observe students’ experience in other years as well as other faculties. This would allow generalize the results on the integration of chatbots in higher education. For example, it is unclear whether students in this study engaged more with the chatbot because they come from a more “technical” discipline and they are already familiar with the use of technology. The outcome may have been different in a humanities course. Also, it is not clear how first year students, who are not yet familiar with the teaching and learning process of the university and the available communication channels, would react to the chatbot service, taking into consideration that first year students are more instructor-dependent (Hagenauer & Volet, 2014 ). Furthermore, it is important to mention that every instructor reacts differently when it comes to provide timely feedback and creating strong group dynamics with their students, even if they share the same workload with their peers. So the attitude of the instructor can also impact the use of the chatbot. Moreover, while the chatbot can be very useful for facts to be learned, which require clear right answers, it is not clear how the chatbot could support learning that is centered around students' developing ideas about a topic or a body of theory. For all the above-mentioned reasons, we are not aiming to make general claims about the use of the chatbot in higher education, but to explore the efficiency of this cost-effective additional student support service and to expand the research in contexts with high student–instructor ratio.

The results of our research confirmed the existing literature that the use of chatbots enhances self-efficacy and learner achievement. Universities should establish Educational Technology Centers managed by subject matter experts to assist instructors in integrating and engaging students with teaching assistant chatbots. To obtain reliable results, instructors with low levels of digital literacy should receive proper training and coaching. Moreover, hands-on workshops should be organized to urge instructors to embrace the teaching assistant chatbot interaction in supporting learning and teaching. Besides, any course instructor who engages a virtual teaching assistant chatbot with students must ensure that each student is conversant with the instant messaging (WhatsApp, Telegram, or WeChat). The instructor must also ensure that the students have consistent Internet access and examine whether specific students cannot use the instant messaging application. Students may have difficulty adapting to the teaching assistant chatbot when using it for the first time. An initial session should be organized in which the engagement procedure is detailed, and students are informed about its assets to alleviate the affirmed challenges.

Certain directions for additional investigations are made based on the findings and discussion of the present study. It remains unclear whether chatbot can support learning by responding to technical questions mainly (e.g. explaining what is HTML5), or it can help learners to understand the conceptual content better. Future research could look into the impact of chatbots in other areas of knowledge. Also, the academic performance of students who interacted with the chatbot the most and those who interacted with it less could be compared. In addition, the chatbot’s longitudinal influence on student engagement and motivation should be investigated. Another identified area of future research is the influence of chatbot use on postgraduate supervision activities. Lastly, future studies could investigate the control cohort’s perceptions and test the acceptance of the experimental cohort using the Unified Theory of Acceptance and Use of Technology models, focusing on the intentions of the students when they use the chatbot, as well as on the subsequent usage behavior.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

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HBE, DV and ATM conceptualized the research. HBE led the ethics approval process. HBE, ATM and PKB constructed the study design and materials used during study collection. EEJ and PKB realized data collection. DV and HBE supervised the data analysis. HBE, ATM, EEJ and PKB wrote the original manuscript draft. DV revised the draft version and created the final version submitted to the journal. All authors contributed in revising the manuscript. All authors read and approved the final manuscript.

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Study reveals inadequate levels of medical assistant staffing in US

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A new paper published in Annals of Family Medicine analyzed the current ratio of medical assistants to primary care physicians within medical practices, finding that health-system-owned practices were less likely to be adequately staffed than other practices.

According to the paper, medical assistants are among the fastest growing occupations within the US primary care workforce, and many medical practices have expanded the roles and caregiving responsibilities of primary care medical assistants.

But not much was previously known about medical assistant staffing ratios across the US. This study was the first to assess staffing ratios and to examine the factors associated with ratios consistent with the teamlet model of primary care. The teamlet model includes two medical assistant health coaches and one primary care clinician.

The study analyzed survey answers from 1,252 primary care practices and determined that more than half (56.6%) of the practices had ratios of one medical assistant to each primary care clinician. Slightly more than one in ten (11.4%) had ratios of two or more assistants per clinician while more than a quarter (27.6%) had less than a one-to-one ratio.

“Adequate medical assistant staffing is needed to support the delivery of patient-centered, high-quality primary care. Medical assistants increasingly provide direct patient support, including health coaching to help patients with managing their chronic conditions and conducting patient outreach activities to ensure the reliable provision of evidence-based and recommended care,” said lead author Hector P. Rodriguez.

“Past research indicates that a staffing ratio of 2-to-1 medical assistants to primary care clinicians is needed for ‘teamlets’ to effectively support the provision of preventive care and help manage chronic care, while continuing to do traditional medical assistant functions, such as taking medical histories and preparing patients for examinations. Our study indicates that few primary care practices (~11%) have the capacity to provide the recommended staffing ratios for the teamlet model.”

Independent practices, medical group–owned practices, and Federally Qualified Health Centers were more likely to have ratios of two or more assistants per clinicians than practices owned by health care systems. Low medical assistant staffing levels were not found to be associated with higher levels of staffing of nurses, physician assistants, and nurse practitioners.

“The current study’s results suggest that system-owned practices may be less able to use patient-centered innovations because they have less medical assistant staffing support compared to independent practices and medical group owned practices,” said Rodriguez. “Health policy advocates are increasingly concerned about the potential harms of physician practice consolidation under health care systems because systems may be less responsive to the local needs of vulnerable populations and inadequately resource primary care practices to effectively address patients’ self-management and navigation support needs. Our hope is that the study helps make the connection between medical assistant staffing and the provision of patient-centered care clear to policy makers and health care organizational decision-makers.”

Additional authors include: Dorothy Y. Hung and Stephen M. Shortell of UC Berkeley School of Public Health and Alena D. Berube and Elliott S. Fisher of the Dartmouth Center for Health Policy & Clinical Practice at Dartmouth College.

This research project was funded by the Robert Wood Johnson Foundation.

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A modern way to teach and practice manual therapy

Affiliations.

• 1 School of Health Sciences, Queens Medical Centre, University of Nottingham, Nottingham, NG7 2HA, UK.
• 2 Allied Health Research Unit, University of Central Lancashire, Preston, PR1 2HE, UK. [email protected].
• 3 Centre of Precision Rehabilitation for Spinal Pain, School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
• 4 Nottingham CityCare Partnership, Bennerley Rd, Nottingham, NG6 8WR, UK.
• 5 School of Medicine, University of Nottingham, Queens Medical Centre, Nottingham, NG7 2HA, UK.
• 6 Department of Orthopaedics, West Herts Hospitals Trust, Watford, WD18 0HB, UK.
• 7 School of Physiotherapy, Manchester Metropolitan University, Manchester, M15 6GX, UK.
• 8 Department of Orthopaedics, Duke University, 200 Morris Street, Durham, NC, 27701, USA.
• 9 School of Sport and Health Sciences, University of Brighton, Darley Rd, Eastbourne, BN20 7UR, UK.
• 10 Clinical Neuroscience, Trafford Building, Brighton and Sussex Medical School, University of Sussex, Brighton, BN1 9PX, UK.
• 11 University College of Osteopathy, 275 Borough High St, London, SE1 1JE, UK.
• 12 Department of Clinical Sciences, Obstetrics and Gynecology, Umeå University, S-90187, Umeå, Sweden.
• 13 The School of Soft Tissue Therapy, Exmouth, Devon, EX8 1DQ, UK.
• 14 Department of health, LUNEX, Differdange, 4671, Luxembourg.
• 15 Luxembourg Health & Sport Sciences Research Institute A.s.b.l., 50, Avenue du Parc des Sports, Differdange, 4671, Luxembourg.
• 16 Department of Occupation and Health, School of Organization and Development, HAN University of Applied Sciences, Nijmegen, the Netherlands.
• PMID: 38773515
• PMCID: PMC11110311
• DOI: 10.1186/s12998-024-00537-0

Background: Musculoskeletal conditions are the leading contributor to global disability and health burden. Manual therapy (MT) interventions are commonly recommended in clinical guidelines and used in the management of musculoskeletal conditions. Traditional systems of manual therapy (TMT), including physiotherapy, osteopathy, chiropractic, and soft tissue therapy have been built on principles such as clinician-centred assessment, patho-anatomical reasoning, and technique specificity. These historical principles are not supported by current evidence. However, data from clinical trials support the clinical and cost effectiveness of manual therapy as an intervention for musculoskeletal conditions, when used as part of a package of care.

Purpose: The purpose of this paper is to propose a modern evidence-guided framework for the teaching and practice of MT which avoids reference to and reliance on the outdated principles of TMT. This framework is based on three fundamental humanistic dimensions common in all aspects of healthcare: safety, comfort, and efficiency. These practical elements are contextualised by positive communication, a collaborative context, and person-centred care. The framework facilitates best-practice, reasoning, and communication and is exemplified here with two case studies.

Methods: A literature review stimulated by a new method of teaching manual therapy, reflecting contemporary evidence, being trialled at a United Kingdom education institute. A group of experienced, internationally-based academics, clinicians, and researchers from across the spectrum of manual therapy was convened. Perspectives were elicited through reviews of contemporary literature and discussions in an iterative process. Public presentations were made to multidisciplinary groups and feedback was incorporated. Consensus was achieved through repeated discussion of relevant elements.

Conclusions: Manual therapy interventions should include both passive and active, person-empowering interventions such as exercise, education, and lifestyle adaptations. These should be delivered in a contextualised healing environment with a well-developed person-practitioner therapeutic alliance. Teaching manual therapy should follow this model.

Keywords: Chiropractic; Evidence-based healthcare; Manual Therapy; Osteopathy; Person-centred healthcare; Physiotherapy; Soft-tissue therapy.

© 2024. The Author(s).

Publication types

• Musculoskeletal Diseases / therapy
• Musculoskeletal Manipulations* / education
• Musculoskeletal Manipulations* / methods

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Key words:  The Standard Model and its extensions, neutrino mass and mixing models, CP and flavor symmetries, leptogenesis, dark matter

Applications to the open call may be submitted by those who meet the conditions specified in Section 113 of the Act – Law on Higher Education and Science of 20 July 2018 (i.e., Journal of Law 2023 item 742). 

KEY RESPONSIBILITIES :

• conducting scientific research in the discipline of physical sciences,
• teaching classes in the 1st cycle, 2nd cycle and doctoral studies for the Physics degree programme and the related programme and the Computer Science degree programme,
• participation in the research projects and grants in the candidate’s country or in the international research projects and grants,
• actively applying for research grants,
• realization of organizational duties in accordance with the Individual Assignment of Duties.

Requirements

QUALIFICATION REQUIREMENTS:

• minimum PhD in the discipline of physical sciences,
• experience in conducting research in the discipline of physical sciences in the scope of phenomenology of the Standard Model of the elementary particles and its extensions,
• participation in the phenomenological research in the scope of elementary particles physics focuses on the neutrino theory,
• recent, high-scoring scientific achievements in the discipline of physical sciences, in the scope of elementary particles indexed in Scopus or Web of Science,
• knowledge of numerical and analytic methods in physics,
• documented international activity,
• experience in realization of research projects,
• documented presentations at the international scientific conferences,
• documented scientific internships or stays abroad,
• knowledge of programming languages, preferably: C++, Python,
• knowledge of computational algebra and the Linux system,
• fluent command of English,
• documented activity in terms of applying for grants in the candidate’s country or international grants,
• scientific research plan,
• experience in teaching classes in the 1st cycle, 2nd cycle and doctoral studies for the Physics degree programme and the related degree programmes,
• command of conducting classes using the tools for distance learning,
• command of Polish that allows for conducting classes or readiness to learn Polish (in the case of foreign candidates),
• fulfilment of the didactic requirements in accordance with the needs of the August Chełkowski Institute of Physics.

ADDITIONAL REQUIREMENTS :

• readiness to take up the role of the leader of a research team,
• readiness for the research/didactic mobility,
• ability to work in a research team,
• self-reliance and good work organization skills,
• proactive attitude and initiative.

Additional Information

TERMS OF EMPLOYMENT:

• full time employment under the fixed period employment contract *
• employment at the University as a primary place of work,
• base remuneration in accordance with the Remuneration regulations of the University of Silesia,
• additional remuneration components in accordance with The Rules and Regulations of Remuneration for Employees of the University of Silesia in Katowice,
• pro-quality bonus for special achievements according to regulations in force,
• the annual teaching load of 210 hours,
• task-based working time,
• opportunity for professional development,
• work environment supporting sustainable development based on the diversity, equality and inclusion,
• social benefits package,
• private medical care as a part of the additional health insurance for the University employees and their families,
• private medical care as a part of additional health insurance for the University employees and their families,
• ability to join the Occupational Pension Scheme and the group pension insurance POGODNA JESIEŃ.

*at the University first employment contract concluded with an academic is the fixed period employment contract for the period of maximum 4 years.

REQUIRED DOCUMENTS:

• motivational letter,
• scientific resume (CV),
• copies of documents confirming candidate’s education (or scans of such documents),
• list of the candidate’s scientific achievements,
• letter of recommendation,
• list of the candidate’s didactic achievements,
• list of the candidate’s achievements in terms of popularization of science,
• other documentation defined in the open call requirements.

SCHEDULE OF THE CALL:

• application submission deadline:  16 th  of June 2024  (11.59 p.m. CET)
• the selection process will be concluded by:  15 th  of August 2024 

OPEN CALL PROCEDURE:

Applications received after the deadline, incomplete applications and applications submitted in any other way than defined in the open call announcement shall not be considered. 

The open call is conducted and solved by the open call committee appointed by the Rector. The open call procedure consists of two stages:

1) the formal assessment of the submitted documents,

2) merit-based evaluation of the candidates.

The formal assessment is preceded by the initial verification of the submitted documents confirming fulfilment of the requirements defined in the open call announcement. In the case of doubts the open call committee may call the applicant to complete the documentation in the defined period of time.

The open call committee prepares a list of persons whose applications fulfil the open call requirements to the greatest extent and invites them for an interview.

The interview may take place in person or by using the electronic means of communication. The invited candidates may be asked to prepare additional materials for the needs of presentation of their candidacy during the interview.

The open call committee may resign from conducting the interview if they state that the applications submitted in the open call do not fulfil the open call requirements.

The open call is solved by way of resolution adopted by the open call committee by secret ballot with the simple majority of votes.

The applicants are notified about the results of the open call. The applicants have a right to appeal against the open call committee’s decision in writing within 7 days from the day they are notified about the result of the open call. The appeal shall be considered by the Rector within the period of maximum 30 days.

The final, binding decision on employment of the candidate selected and recommended by the open call committee is made by the Rector.

The University reserves the right to leave the call unresolved.

IMPORTANT INFORMATION:

The condition necessary for employment of the candidate selected in the selection proceedings is the fulfilment of the obligations arising from Section 265(4), (5), (13) of the Act –  Law on Higher Education and Science  of 20 July 2018 (Journal of Law of 2023 item 724).

In the case of applying for employment at the University of Silesia in Katowice as the primary place of work, candidates employed in other higher education institutions as their primary place of work, should, as of the day of their employment, fulfil the requirement defined in art. 120, section 2 of the Act –  Law on Higher Education and Science  of 20 July 2018 (Journal of Law of 2023 item 742), according to which an academic may have  only one  primary place of work at a time.

In the case of person selected as a result of the open call selection procedure, who has obtained their academic degree, their degree in arts or their professional title abroad, and whose academic degree, degree in arts or their professional title has not been recognized as equivalent with the adequate Polish degree or title, employment of such person is possible in accordance with art. 116 section 2a of the Act –  Law on Higher Education and Science  of 20 July 2018 (Journal of Law of 2023 item 742), in accordance with the procedure in force at the University or on the basis of the nostrification procedure, for which the candidate shall apply. 

Work Location(s)

Where to apply.

• ENGINEERING
• ARTS & SCIENCE
• POLYTECHNIC
• JUNIOR COLLEGE

• Assistant Professor
• Associate Professor
• Commerce & Management
• Political Science
• Private Jobs
• Teaching Jobs
• West Bengal

Applications are invited from interested and eligible candidates for the following Positions. Interested Eligible candidates fulfilling the criteria may submit their applications in the prescribed format along with the detailed CV / As per the Norms.

TEACHING FACULTY RECRUITMENT 2024 | FACULTY TICK

📅 Date of Advertisement:

🏢 NAME OF THE INSTITUTION

Indian institute of legal studies – teaching faculty recruitment 2024, 🏫 about institution.

The Indian Institute of Lega1 Studies situated at the foothills of the Himalayas in the Terai- Dooars confluence bestowing it with natural landscape, sharing boundaries with the SAARC nations. The Indian Institute of Legal Studies is affiliated to the University of North Bengal and is approved by the Bar Council of India and recognized under Sections 2(f) & 12 (B) of the University Grants Commission Act, 1956. The Institute is accredited by the National Assessment and Accreditation Council (NAAC). The Institute offers Undergraduate Law programs for 5 years integrated courses in B.A. LL. B. (Hons), B.com. LL.B. (Hons.) and B.B.A. LL-B. (Hons.) and 3 years LL.B. It offers Post Graduation courses of 2 years LL.M. program and 2 years Master’s program in Public Administration and Governance.

Indian Institute of Legal Studies Darjeeling, West Bengal Invites Application for the following Positions of Principal / Associate Professor/ Assistant Professors Recruitment 2024

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💼 DESIGNATION / JOB POSITION

🎯 department / discipline / specialization.

• Associate Professor Of Law
• Assistant Professor Of Law, Economics, Commerce, Political Science, Sociology, English And Management

🎓 QUALIFICATION & EXPERIENCE / REQUIREMENT

• For Assistant Professor, a good academic record: o With a PhD Degree in the concerned /allied/relevant disciplines, or o A Master’s Degree with at least 55% marks (or an equivalent grade in a point-scale, wherever the grading system is followed) with UGC-NET/JRF/SET. Candidates with 2-3 years’ experienceare preferred. [The fulfilment of these conditions is to be certified by the Registrar or the Dean (Academic Affairs) of the university concerned].
• For Associate Professor a good academic record: o with a mandatory PhD Degree in the concerned/allied/relevant disciplines o a Master’s Degree with at least 55% marks (or an equivalent grade in a point-scale, wherever the grading system is followed) with UGC-NET/JRF/SET. o additionally require a minimum of 8 years of experience of teaching and/or research in an academic/research position equivalent to that of Assistant Professor in a University, College or Accredited Research Institution/industry with a minimum of seven publications in the peer-reviewed or UGC-listed journals.

Provided, the candidates registered for the PhD programme prior to July 11, 2OO9 shall be governed by the provisions of the then existing Ordinances/Bye-laws/Regulations of the Institution awarding the degree and such PhD candidates shall be exempted from the requirement of NET/ SLET/ SET for recruitment and appointment of Assistant professor or equivalent positions in Universities/ Colleges/ Institutions subject to the fulfilment of the following conditions. • The PhD degree of the candidate has been awarded in a regular mode; • The PhD thesis has been evaluated by at least two external examiners; • An open PhD viva voce of the candidate has been conducted; • The Candidate has published two research papers from his/her PhD work out of which at least one is in a refereed journal; • The candidate has presented at least two papers based on his/her Ph.D. work in conferences/ seminars sponsored/funded/supported by the UGC/ICSSR/ CSR or any similar agency The fulfilment of these conditions is to be certified by the Registrar or the Dean (Academic Affairs) of the university concerned.

• For Principal, a good academic record: o with a mandatory PhD Degree in the concerned/allied/relevant discipline; o Professor/Associate Professor with a total service/ experience of 15 years of teaching/research/ administration in Universities, Colleges and other institutions of higher education; o A minimum of 120 Academic/Research Score as per Assessment criteria and methodology prescribed under UGC Regulations, 2018.

Provided, the candidates registered for the PhD programme prior to July 11, 2OO9 shall be governed by the provisions of the then existing Ordinances/Bye-laws/Regulations of the Institution awarding the degree and such PhD candidates shall be exempted from the requirement of NET/ SLET/ SET for recruitment and appointment of Assistant professor or equivalent positions in Universities/ Colleges/ Institutions subject to the fulfilment of the following conditions. • The PhD degree of the candidate has been awarded in a regular mode; • The PhD thesis has been evaluated by at least two external examiners; • An open PhD viva voce of the candidate has been conducted; • The Candidate has published two research papers from his/her PhD work out of which at least one is in a refereed journal; • The candidate has presented at least two papers based on his/her Ph.D. work in conferences/ seminars sponsored/funded/supported by the UGC/ICSSR/ CSR or any similar agency The fulfilment of these conditions is to be certified by the Registrar or the Dean (Academic Affairs) of the university concerned

🏆 SALARY / REMUNERATION / PAY SCALE ₹

As Per Norms

💺 JOB LOCATION

Darjeeling, West Bengal

Find More Opportunities

There are 28 states and 6 union territories available in India. Each state is providing job opportunities for job seekers in various educational institutions .

facultytick.com is a India’s Top Job Portal for all Government and  Private Sector Jobs Providing you the information regarding the updates of Latest Teaching and Non Teaching Jobs from every states. Every year, newer vacancies are released in the state and central government to give opportunities. Find out what it’s like to build your career at all state and get connected to our latest job opportunities and events.

📝 HOW TO APPLY

Interested candidates are required to submit their application along with updated CV to [email protected] within May 28, 2024.

[email protected]

Candidates shall enclose the following documents:

• Good Quality Scan copy of SSLC Certificate / Submit their Proof of Age. (.pdf file)
• Good Quality Scanned copy of UG / PG Certificates; PhD /NET/JRF Degree (Pdf file).
• Good Quality Scan copy of Consolidated or Semester wise mark Sheets (pdf file)
• Good Quality Scan copy of All Previous Experience Certificates (Teaching, Industry & Research) (.pdf file)
• Good Quality scan copy of pay slip of last pay drawn or salary certificate or Bank statement (pdf file)
• Good Quality Scan copy of: One passport Size photograph
• Updated Resume (pdf file)

Finding the Latest Academic and Non Academic Jobs for your career. Academic Jobs aspirants get latest Government Job updates from Central Government, State Government and Private Institutions Job Updates Notification. facultytick.com Here you can search and apply for latest Teaching and Non Teaching Jobs in India .

📅 IMPORTANT DATE

The last date for receiving applications is On or Before May 28, 2024.

📫 CONTACT INFORMATION

📠 PHONE NUMBER

+91 62969 03461

Looking for the  most up-to-date Teaching Faculty and Non Teaching Job vacancies in INDIA List of Teaching and Non Teaching career options in India. The job opportunities sections of newspapers and employment websites advertise thousands of positions all over INDIA .

📌 ADDRESS FOR COMMUNICATION

Indian Institute of Legal Studies Dagapur, Siliguri, P.O. Salbari, P.S. Matigara, Darjeeling, West Bengal, India 734002, INDIA

📣 OFFICIAL SOURCE

Indian Institute of Legal Studies Dagapur Teaching Faculty and Principal positions Recruitment Official Notification by HR Department on 20th May 2024

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🔖 Post Expires on 28 th May 2024

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