Generative AI-powered social robots in education
opportunities and challenges from a Delphi study
Gabriella Tisza (Eindhoven University of Technology)
Panos Markopoulos (Eindhoven University of Technology)
Sofia Serholt (University of Gothenburg)
Jauwairia Nasir (Ausburg University)
Omar Mubin (Western Sydney University)
Adriana Tapus (ENSTA Paris Institut Polytechnique de Paris)
Salvatore Anzalone (l'Enseignement et de la Formation chez Province de Liège)
Paul A. Vogt (University Medical Center Groningen)
Mark A. Neerincx (TU Delft - Interactive Intelligence)
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Abstract
The rise of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) is accelerating the integration of social robots into education. These technologies enhance robots' abilities in natural language interaction, adaptive behaviour, and personalised learning support. To advance real-world implementation, it is essential to identify the main challenges and opportunities in this field. We conducted a two-round Delphi study with 16 experts in human-robot interaction and educational technology. In the first round, participants outlined opportunities, challenges, and potential robot roles expected in the short term (1 year) and medium term (5 years). Content analysis revealed 8 opportunities, 10 challenges and 10 roles. In the second round, experts ranked their importance and feasibility across both time horizons. The results show that the most critical opportunities and challenges are also the least feasible to achieve in practice. Conversely, the proposed roles of educational robots demonstrated alignment between importance and feasibility. Experts highlighted three promising roles for robots in the GenAI era: supporting teachers in boosting learner engagement, serving as conversational interfaces for students to access knowledge and assisting teachers in supporting disadvantaged learners. These findings provide a roadmap for prioritising feasible innovations in educational robotics.
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