Literature review: Research on AI in higher education

Introduction

Contemporary advancement in information technology informs educational systems, ranging from local schools to teacher education programs within universities and higher education. This reality creates expectations and requirements on teacher educators to be well versed in what is described as critical skills. To be able to handle and teach students to handle an ever-growing flow of information, often in the form of fragmented parts only loosely connected. General and generic abilities have grown in importance. These abilities include being able to express oneself both in writing and speech, to reflect and to critically evaluate information, to analyse knowledge claims and source material. There is also a more direct kind of firsthand technological knowledge relating to the use of digital tools, often being taught to teacher candidates (Adenling & Olsson, 2011). 

Given the rise of new ai-driven information systems, machine learning and media technologies, such as information bots and deepfakes with capacity to spread disinformation, one example among many relating to the challenges AI pose to the higher education system is made visible.

Purpose and delineation

This literature review aims to map the scientific discussion concerning digital technology with a focus on AI in higher education and specifically teacher education, and the role it plays in forming the knowledge base for teachers. The review will discuss tensions and disharmonies between the two. In other words, focus is placed on content and analysis regarding AI technology segments of growing educational knowledge for teachers being negotiated in higher teacher education. Relating to these educational perspectives the scope of the review includes research on the wide topic of AI, with four elements of specific interest for analysis: 

1. Understanding, handling, and analysing information. 
2. Expectations and competencies relating to teachers. 
3. Changes in knowledge relating to teacher education and to the teaching profession. 
4. Changes in universities and institutions of teacher education.

Method

The material below provides a part of a future comprehensive literature review on the aforementioned themes. The review skews toward the informal and configurative types of literature reviews, without being identified as exclusively one or the other. The search string being used was “Ai in teacher education”. The filters used were “Year: 2022-2023”, “Open access”, “Peer Reviewed”, “Education and Educational Research”, “Teaching”, “Artificial intelligence” and “Higher education”. The HB Primo search service allowed for access to a host of resources, including library material, electronic sources, research articles, e-magazines and e-books. From the large catalogue of resources, the literature review focuses on research articles, linking the topics and aims of interest to an ongoing academic debate on the subject. A focus on articles in research journals allows the review to reflect academic debate in a fast-paced format, mirroring the velocity and momentum of technology production today. Additional articles and sources were identified from the reference lists in relevant publications, allowing for a more systematic approach during that phase of the process, even though the review itself is not systematically produced (Parding & Berg-Jansson, 2015).

Positional graph of review

Research on AI in teacher education 

Several themes and disharmonies can be identified concerning the relation between AI in teacher education and the professional knowledge base of teachers. From a more general perspective AI have during the twenty-first century frequently been suggested to enhance educational activities and processes (Humble & Mozelius, 2022); a reoccurring theme in the review. A tension between such educational vision in teacher education, in relation to professional teacher competencies, the knowledge base and practical implementation on a school level is evident; the ambition of one does not match the contents of the other (Ng et al., 2023; Karsenti, 2019). Pertaining to higher education and therefore also teacher education, the issue of using AI to grade student papers has received much more attention after the release of ChatGPT (Kumar, 2023), simultaneously the Great Online Transition (GOT) placed new focus on teachers’ digital competencies in higher education (Howard & Tondeur, 2023). A tension may be identified between the teacher education programs and the knowledge base of the teaching profession relating to this development; if higher education and teacher education along with it changes to technological grading, the competencies and knowledge used to educate candidate teachers in grading may be undermined. This would in turn affect the knowledge base of teachers, undermining the ability to acquire the skills needed to manually grade student assignments. Also relating to GOT, Starkey et al. concludes that higher education teachers require competence in suitable pedagogical practice positioned in line with disciplinary culture and the technologies available (Starkey et al., 2023). In other words, not only teacher educators but also educators in different fields of higher education need to take part in researching and developing pedagogies supported by evolving contemporary AI technologies (Asakura et al., 2020). Implications of technology for knowledge production, learning and teaching can thus also be seen from a broader perspective. 

A noticeable tendency of a majority of the articles is to advocate optimism specifically relating to digital tools and AI for teaching, enhancing skills and quality in higher education. Digital tools allow for student autonomy (Almufarreh & Arshad, 2023), new teaching methods relating to technology and AI allows for improved critical thinking skills for students (Muthmainnah et al, 2022), and AI and machine learning may enable quality improvements relating to teaching and learning in higher education (Nawaz et al., 2022). Substantial technological optimism in higher education generally and in teacher education specifically may create tensions relating to the knowledge base of the teaching profession. Aside from difficulties of local implementation of technology in schools, a crowded curriculum may not be beneficial for teacher candidates for learning and educational purposes. It should be mentioned that the divide between researchers relating to this technological optimism has been widely discussed. Player-Koro analyses this topic in relation to digitalisation and in particular ICT. Here there’s a polarisation between different views in academia. In one camp there is a school of optimism in relation to education and technology, specifically in terms of the successful and constructive use of technology in education. In the other camp we see a more realistic attitude, maybe even a more pessimistic approach when it comes to using educational technology for teaching (Player-Koro, 2012). 

Side by side with a technological optimism, a technological determinism is detectable relating to using digital tools and AI both in teacher and school education. Ai technology is here to stay and constitutes the new order going forward. For instance, the new era of AI is understood to have a huge impact on role cognition in the traditional education system, forcing teachers to set up a new notion of talents, students, teaching concepts, and reformulate the role of teachers (Zhu & Ren, 2022). There is a need for scholars in higher educational domains and fields to incorporate the application of AI technologies with educational theories; relating to teacher education, such perspectives tend to be more preferred by experts in educational fields in higher education (Chen et al., 2020). In connection, AI may be used as a solution to expand productivity in online learning and to connect students and instructors in asynchronous online environments, breaking the spatiotemporal barriers to learning (Goksel & Bozkurt, 2019). When ideas such as these influence teacher education the relation between teacher education and contemporary professional teacher knowledge experience disharmony. Teachers may not be knowledgeable and competent enough to use AI-driven educational applications for teaching and learning purposes and may lack technological expertise to conduct necessary data analysis or to create principles for automatically generating assignments (Ng, Leung et al., 2023). Additionally relating to the knowledge base of teachers, questions of AI-based misunderstanding, limitations, and hidden ethical issues behind the use of different platforms have been identified (ibid.) and may constitute a discrepancy between change in teacher education and the knowledge base of professional teachers. In other words, alongside a shortage of technological knowledge, a deficit in teachers’ ethical awareness relating to implementation of AI technology is distinguishable. These are issues teacher education needs to address by bridging the gaps (Akgun & Greenhow, 2022; Luan et al., 2020). Simultaneously, research indicates that an essential aspect of influence on pre-service teachers’ technology integration during the time of attending their teacher education programs, is the quality and quantity of technological experiences offered by their teacher education institutes (Farjon et al., 2019). This reality would affect both the knowledge developed within teacher education, as well as teachers’ professional knowledge base after work started. However, related to this is the fact that studies rarely reveal which specific digital competencies teachers need to become qualified in an AI-driven learning environment (Ng, Leung et al., 2023), pointing to an ambiguity concerning the professional knowledge base of teachers. At any rate, course design is indicated to play a substantial role when implementing learning structures in teacher education (Kostiainen et al., 2018), which would apply to competencies in AI as well. Such structures would benefit from teacher education institutions taking seriously the risk of future teachers having to negotiate opposing approaches to inclusion and academic attainment (Essex et al., 2021), pointing to an even larger challenge and tension between teacher education and professional teacher knowledge. Teachers are not only to understand AI from a perspective of knowledge and ethics, but also from a perspective of student inclusion. 

To sum up, contemporary advancement in digital technology and specifically AI seem to have a substantial impact on higher education in general and notably on teacher education specifically, as well as on working professional schoolteachers. The review illustrates several expectations, tensions and disharmonies concerning the relation between AI driven changes in teacher education and the situation for and knowledge base relating to the teaching profession.

References 

Adenling, Elinor & Olsson, Johanna (2011). Erfarenheter av portfoliometodiken inom nätutbildning: Kritiska reflektioner och ”halleluja moments”. Högre Utbildning, 1(2), 77-87. 

Beach, D., & Bagley, C. (2012). The weakening role of education studies and the re-traditionalisation of Swedish teacher education. Oxford Review of Education, 38(3), 287–303. https://doi.org/10.1080/03054985.2012.692054

Beach, D., Bagley, C., Eriksson, A., & Player-Koro, C. (2014). Changing teacher education in Sweden: Using meta-ethnographic analysis to understand and describe policy making and educational changes. Teaching and Teacher Education, 44, 160–167. https://doi.org/10.1016/j.tate.2014.08.011 

Jalali, S., & Wohlin, C. (2012). Systematic literature studies: Database searches vs. backward snowballing. Proceedings of the ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, 29–38. https://doi.org/10.1145/2372251.2372257 

Newton, P. M., Da Silva, A., & Berry, S. (2020). The Case for Pragmatic Evidence-Based Higher Education: A Useful Way Forward? Frontiers in Education, 5, 583157. https://doi.org/10.3389/feduc.2020.583157 

Parding, K. Å., & Berg-Jansson, A. (2016). Teachers’ Working Conditions Amid Swedish School Choice Reform: Avenues for Further Research. Professions and Professionalism, 6(1). https://doi.org/10.7577/pp.1416 

Player-Koro, C. (2012). Reproducing traditional discourses of teaching and learning mathematics: Studies of mathematics and ICT in teaching and teacher education. Department of applied IT, University of Gothenburg. 

References: Literature review 

Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431–440. https://doi.org/10.1007/s43681-021-00096-7 
Almufarreh, A., & Arshad, M. (2023). Promising Emerging Technologies for Teaching and Learning: Recent Developments and Future Challenges. Sustainability, 15(8), 6917. https://doi.org/10.3390/su15086917

Asakura, K., Occhiuto, K., Todd, S., Leithead, C., & Clapperton, R. (2020). A Call to Action on Artificial Intelligence and Social Work Education: Lessons Learned from A Simulation Project Using Natural Language Processing. Journal of Teaching in Social Work, 40(5), 501–518. https://doi.org/10.1080/08841233.2020.1813234 

Chen, P. (2022). Design and Construction of an Interactive Intelligent Learning System for English Learners in Higher Education Institutions. Advances in Multimedia, 2022, 1–8. https://doi.org/10.1155/2022/6364796 

Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020). Application and theory gaps during the rise of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002 

Essex, J., Alexiadou, N., & Zwozdiak-Myers, P. (2021). Understanding inclusion in teacher education – a view from student teachers in England. International Journal of Inclusive Education, 25(12), 1425–1442. https://doi.org/10.1080/13603116.2019.1614232 

Farjon, D., Smits, A., & Voogt, J. (2019) Technology integration of pre-service teachers Exaplained by attitudes and beliefs, competency, access, and experience. Computers & Education, 130, 81-93, https://doi.org/10.1016/j.compedu.2018.11.010 

Howard, S. K., & Tondeur, J. (2023). Higher education teachers’ digital competencies for a blended future. Educational Technology Research and Development, 71(1), 1–6. https://doi.org/10.1007/s11423-023-10211-6 

Hrastinski, S., Olofsson, A. D., Arkenback, C., Ekström, S., Ericsson, E., Fransson, G., Jaldemark, J., Ryberg, T., Öberg, L.-M., Fuentes, A., Gustafsson, U., Humble, N., Mozelius, P., Sundgren, M., & Utterberg, M. (2019). Critical Imaginaries and Reflections on Artificial Intelligence and Robots in Postdigital K-12 Education. Postdigital Science and Education, 1(2), 427–445. https://doi.org/10.1007/s42438-019-00046-x 

Humble, N., & Mozelius, P. (2022). The threat, hype, and promise of artificial intelligence in education. Discover Artificial Intelligence, 2(1), 22. https://doi.org/10.1007/s44163-022-00039-z 

Karsenti, T. (2019). Artificial intelligence in education:The urgent need to prepare teachers for tomorrow’sschools. Formation et Profession, 27(1), 105. https://doi.org/10.18162/fp.2019.a166 

 

Korteling, J. E. (Hans)., Van De Boer-Visschedijk, G. C., Blankendaal, R. A. M., Boonekamp, R. C., & Eikelboom, A. R. (2021). Human- versus Artificial Intelligence. Frontiers in Artificial Intelligence, 4, 622364. https://doi.org/10.3389/frai.2021.622364 

Kostiainen, E., Ukskoski, T., Ruohotie-Lyhty, M., Kauppinen, M., Kainulainen, J., & Mäkinen, T. (2018). Meaningful learning in teacher education. Teaching and Teacher Education, 71, 66–77. https://doi.org/10.1016/j.tate.2017.12.009 

Kshirsagar, P. R., Jagannadham, D. B. V., Alqahtani, H., Noorulhasan Naveed, Q., Islam, S., Thangamani, M., & Dejene, M. (2022). Human Intelligence Analysis through Perception of AI in Teaching and Learning. Computational Intelligence and Neuroscience, 2022, 1–9. https://doi.org/10.1155/2022/9160727 

Kumar, R. (2023). Faculty members’ use of artificial intelligence to grade student papers: A case of implications. International Journal for Educational Integrity, 19(1), 9. https://doi.org/10.1007/s40979-023-00130-7 

Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S. J. H., Ogata, H., Baltes, J., Guerra, R., Li, P., & Tsai, C.-C. (2020). Challenges and Future Directions of Big Data and Artificial Intelligence in Education. Frontiers in Psychology, 11, 580820. https://doi.org/10.3389/fpsyg.2020.580820 

Muthmainnah, Ibna Seraj, P. M., & Oteir, I. (2022). Playing with AI to Investigate Human-Computer Interaction Technology and Improving Critical Thinking Skills to Pursue 21st Century Age. Education Research International, 2022, 1–17. https://doi.org/10.1155/2022/6468995 

Nawaz, R., Sun, Q., Shardlow, M., Kontonatsios, G., Aljohani, N. R., Visvizi, A., & Hassan, S.-U. (2022). Leveraging AI and Machine Learning for National Student Survey: Actionable Insights from Textual Feedback to Enhance Quality of Teaching and Learning in UK’s Higher Education. Applied Sciences, 12(1), 514. https://doi.org/10.3390/app12010514 

Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137–161. https://doi.org/10.1007/s11423-023-10203-6 

Shi, Y., & Guo, F. (2022). Exploring Useful Teacher Roles for Sustainable Online Teaching in Higher Education Based on Machine Learning. Sustainability, 14(21), 14006. https://doi.org/10.3390/su142114006 

Sisman-Ugur, S., & Kurubacak, G. (Eds.). (2019). Handbook of Research on Learning in the Age of Transhumanism: IGI Global. https://doi.org/10.4018/978-1-5225-8431-5 

 

Starkey, L., Yates, A., De Roiste, M., Lundqvist, K., Ormond, A., Randal, J., & Sylvester, A. (2023). Each discipline is different: Teacher capabilities for future-focussed digitally infused undergraduate programmes. Educational Technology Research and Development, 71(1), 117–136. https://doi.org/10.1007/s11423-023-10196-2 

 

Wogu, I. A. P., Misra, S., Olu-Owolabi, E. F., Assibong, P. A., & Udoh, O. D. (n.d.). Artificial Intelligence, Artificial Teachers and the Fate of Learners in the 21st Century Education Sector: Impli- cations for Theory and Practice. 

 Zhu, J., & Ren, C. (2022). Analysis of the Effect of Artificial Intelligence on Role Cognition in the Education System. Occupational Therapy International, 2022, 1–11. https://doi.org/10.1155/2022/1781662