Context and Objectives

This panel aims to explore the impact of Artificial Intelligence (AI) on education and training practices and its potential to transform learning environments. The goal is to bring together experts from different countries to discuss the challenges and opportunities AI presents in education, its applications in teaching and training, and its role in educational research: insights from case studies and perspectives on AI in education and training.

The panel will be structured as follows:

1. Introduction of panelists: Each speaker will introduce themselves, their institution, and their interest in AI in education.

2. Individual contributions: Each panelist will present a practice, research project, or concerns related to AI in education.

3. Panel discussion: Interactive exchanges between panelists to foster debate and critical discussion.

4. Audience interaction: A 30-minute Q&A session.

Contributions

The panel will address various research perspectives. Dr. Gaëlle Molinari (University of Geneva) will explore the impact of Generative AI (GAI) on skill transformation and hybrid intelligence. As the head of the TEPEE (TEchnologies for Positive lEarning Experiences) research team at TECFA (Faculty of Psychology and Educational Sciences), she leads studies on the integration of GAI in learning and training contexts. Her research investigates key issues such as the transformation of academic and professional competencies, the dynamics of deskilling and upskilling induced by automation, and the influence of AI on collaborative learning and knowledge co-construction within groups. In her presentation, she will frame these discussions through the lens of hybrid intelligence, a concept that promotes the co-evolution of human intelligence and AI capabilities rather than a simple substitution of human expertise. This perspective invites a reflection on how AI can be integrated into educational practices in a way that enhances, rather than diminishes, human agency and cognitive development.

Dr. Bengi Birgili (MEF University, Istanbul) will focus on AI-based assessment, particularly comparing open-ended vs. multiple-choice evaluations. Her research suggests that open-ended questions promote cognitive strategies and self-assessment, while AI-assisted evaluation models can enhance assessment efficiency and reduce bias.

Dr. Kah Loong Chue (National Institute of Education, Singapore) will investigate how AI can support self-regulated learning by offering personalised feedback, facilitating self-assessment, and improving student well-being. Most SRL models involve forethought (setting goals, developing strategies), performance (monitoring and adjustment), and self-reflection (evaluation and future goal-setting). Self-assessment also follows similar principles (defining criteria, seeking feedback, and reflecting). The idea is to leverage Generative AI (GAI) interactions to provide external guidance, feedback, and encouragement.

Finally, Dr. Philippe Gabriel (University of Avignon) will examine the overall potential of AI in improving education and training through recent research and technological advancements. His contribution will be framed within the theoretical perspective of “learning lives” which emphasises the continuity of learning across formal (academic training) and informal (self-directed learning, professional experiences) contexts. This framework highlights learners' active role in appropriating technological tools. Additionally, research on digital tool adoption in adult learning underscores the importance of digital artefacts in shaping learning pathways and the need for educators to act as mediators in technological environments. AI’s impact on skill renewal will also be explored through the lens of upskilling and downskilling dynamics, raising questions about AI’s dual role in enhancing pedagogical practices while potentially diminishing educator autonomy through task automation.

This interactive panel will provide insights into the latest developments in AI in education while fostering critical discussions on its transformative impact on pedagogy and assessment.

Barros, A., Prasad, A., & Śliwa, M. (2023). Generative artificial intelligence and academia: Implication for research, teaching and service. Management Learning, 54(5), 597-604. https://doi.org/10.1177/13505076231201445 Emprin, F., & Richard, P. R. (2023). Intelligence artificielle et didactique des mathématiques : état des lieux et questionnements. Annales de Didactique et de Sciences Cognitives, 28. http://journals.openedition.org/adsc/3286; DOI: https://doi.org/10.4000/adsc.3286 Erstad, O., Gilje, Ø., Sefton‐Green, J., & Vasbø, K. (2009). Exploring ‘learning lives’: Community, identity, literacy and meaning. Literacy, 43(2), 100-106. https://doi.org/10.1111/j.1741-4369.2009.00518.x Lodge, J. M., Yang, S., Furze, L., & Dawson, P. (2023). It’s not like a calculator, so what is the relationship between learners and generative artificial intelligence?, Learning: Research and Practice, 9(2), 117-124. DOI: 10.1080/23735082.2023.2261106 Membrive, A., Silva, N., Rochera, M. J., & Merino, I. (2022). Advancing the conceptualization of learning trajectories: A review of learning across contexts. Learning, Culture and Social Interaction, 37, 100658. https://doi.org/10.1016/j.lcsi.2022.100658 Mollick, E. R., & Mollick, L. (2023). Using AI to implement effective teaching strategies in classrooms: Five strategies, including prompts. The Wharton School Research Paper. Available at SSRN: https://ssrn.com/abstract=4391243 Panadero, E. (2017). A Review of Self-regulated Learning: Six Models and Four Directions for Research. Frontiers in Psychology, 8. doi: https://doi.org/10.3389/fpsyg.2017.00422 Vangrunderbeeck, P. (2024). Intégrer l’IA générative dans les stratégies pédagogiques (B. Raucent & P. Wouters, Éds. ; UCLouvain). https://oer.uclouvain.be/jspui/bitstream/20.500.12279/1089.3/6/CahierLLL_IAG_OKOER.pdf Yan, Z., & Brown, G. T. L. (2017). A cyclical self-assessment process: Towards a model of how students engage in self-assessment. Assessment & Evaluation in Higher Education, 42(8), 1247-1262. doi:https://doi.org/10.1080/02602938.2016.1260091 Zapata-Rivera, D., Torre, I., Lee, C., Sarasa-Cabezuelo, A., Ghergulescu, I., & Libbrecht, P. (2024). Editorial: Generative AI in education. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1532896