The Future of Feedback: How Can AI Help Transform Feedback to Be More Engaging, Effective, and Scalable?
Abstract
With digital learning environments becoming more prevalent, the ease with which generative AI enables the scalable production of real-time, automated feedback holds the potential to reshape learning and teaching experiences. This meeting report synthesizes the interdisciplinary perspectives of 50 scholars from educational psychology, computer science, science education, and the learning sciences on the use of generative AI for feedback and its promises and risks in educational practice. We highlight points of convergence in the scholarship, identify areas of debate and unresolved challenges, and outline open questions and future directions for research and educational practice that emerged from structured small-group activities designed to bridge disciplinary barriers.
Keywords
Cite
@article{arxiv.2603.12463,
title = {The Future of Feedback: How Can AI Help Transform Feedback to Be More Engaging, Effective, and Scalable?},
author = {Jennifer Meyer and Olaf Köller and Thorben Jansen and Johanna Fleckenstein and Michael W. Asher and Sarah Bichler and Laura Brandl and Jasmin Breitwieser and Kai S. Cortina and Mutlu Cukurova and Martin Daumiller and Hannah Deininger and Frank Fischer and Dragan Gašević and Jeanine Grütter and Anna Hilz and Ioana Jivet and Jelena Jovanović and Rene F. Kizilcec and Livia Kuklick and Marlit Annalena Lindner and Anastasiya Lipnevich and Ute Mertens and Detmar Meurers and Kou Murayama and Tanya Nazaretsky and Knut Neumann and Ernesto Panadero and Maciej Pankiewicz and Zachary A. Pardos and Chris Piech and Hannah Pünjer and Nikol Rummel and Marlene Steinbach and Olga Viberg and Naomi Winstone},
journal= {arXiv preprint arXiv:2603.12463},
year = {2026}
}