Maria Kutar, University of Salford, UK
Eleni Tzouramani, University of the West of Scotland, UK
Gamila Shoib, University of Bath, UK
Track Call
Artificial Intelligence (AI) has emerged as a powerful tool with the potential to change education as we know it. Its impact spans various dimensions including personalised learning by dynamically adapting teaching material (Chen et al. 2020) and enhanced engagement with immediate feedback (Holmes et al. 2019). AI can also support educators with mundane administrative tasks as well as learning analytics insights, enabling more effective teaching practices and allowing more time for learning-focused and student-centred activities and interactions (Luckin et al. 2016). However, AI also presents significant challenges that need careful consideration, such as the risk of widening the digital divide (Williamson 2019) or perpetuating biases if it is not carefully designed and continuously monitored (West et al. 2018, Williamson 2019). There is also the question of maintaining trust through the use of reliable and pedagogically sound materials (Tuomi et al. 2023). Addressing the challenges requires a collaborative effort among students, educators, policy makers and AI developers (Tuomi et al. 2023, Zawacki-Richter et al. 2019). Significant questions have been raised about whether we need to go back to basics and rethink pedagogy and ethics. Fostering a balanced approach that incorporates ethical considerations and supports equitable access, the educational sector could be a way forward to harness AI’s potential and enhance educator and student teaching and learning experiences.
Track Areas
Papers in the Education Track may present research and applications of teaching and learning practices, teaching innovation practices, teaching cases or other aspects related to an Information Systems (IS) curriculum. Teaching cases should follow the format provided at https://www.ukais.org/teaching-and-learning/award-for-is-teaching-cases/; other paper submissions may follow a standard paper format. We welcome papers based on the full spectrum of methodologies, qualitative, quantitative, or mixed methods.
In the education track we are interested in both learner and educator voices and exploring questions on AI and education including but not limited to:
- The impact of AI on the student learning experience
- Maintaining a balance between AI- led and human-led education
- Case Studies demonstrating the application of AI in education
- Ethical considerations including rights and responsibilities in privacy, data security, transparency and accountability
- Best practices for integrating AI for different learning needs and approaches
- Opportunities for supporting lifelong learning through AI-enhanced education
- Policy and regulation for ethical and equitable use of AI in education.
References
Chen, L., Chen, P. and Lin, Z. (2020) Artificial Intelligence in Education: A review. IEEE Access, 8: 75264-75278
Holmes, W., Bialik, M. and Fadel, C. (2023) Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Globethics Publications.
Luckin, R., Holmes, W., Griffiths, M. and Forcier, L.B. (2016) Intelligence Unleashed: An argument for AI in Education. Pearson Education.
Tuomi, I., Cachia, R. and Villar-Onrubia, D. (2023) On the futures of technology in education: Emerging trends and policy implications. Publications Office of the European Union. 2760: 079734.
West, S.M., Whittaker, M. and Crawford, K. (2018) Discriminating systems: Gender, race and power in AI. AI Now Institute Report.
Williamson, B. (2019) Policy networks, performance metrics and platform markets: Charting the expanding data infrastructure of higher education. British Journal of Educational Tehcnology, 50(6): 2794-2809.
Zawacki-Richter, O., Marín, V.I., Bond, M. and Gouverneur, F. (2019) Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1): 1-27.