The Future of Feedback: How Can AI Help Transform Feedback to Be More Engaging, Effective, and Scalable?
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2026
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| author | Meyer, Jennifer Köller, Olaf Jansen, Thorben Fleckenstein, Johanna Asher, Michael W. Bichler, Sarah Brandl, Laura Breitwieser, Jasmin Cortina, Kai S. Cukurova, Mutlu Daumiller, Martin Deininger, Hannah Fischer, Frank Gašević, Dragan Grütter, Jeanine Hilz, Anna Jivet, Ioana Jovanović, Jelena Kizilcec, Rene F. Kuklick, Livia Lindner, Marlit Annalena Lipnevich, Anastasiya Mertens, Ute Meurers, Detmar Murayama, Kou Nazaretsky, Tanya Neumann, Knut Panadero, Ernesto Pankiewicz, Maciej Pardos, Zachary A. Piech, Chris Pünjer, Hannah Rummel, Nikol Steinbach, Marlene Viberg, Olga Winstone, Naomi |
| author_facet | Meyer, Jennifer Köller, Olaf Jansen, Thorben Fleckenstein, Johanna Asher, Michael W. Bichler, Sarah Brandl, Laura Breitwieser, Jasmin Cortina, Kai S. Cukurova, Mutlu Daumiller, Martin Deininger, Hannah Fischer, Frank Gašević, Dragan Grütter, Jeanine Hilz, Anna Jivet, Ioana Jovanović, Jelena Kizilcec, Rene F. Kuklick, Livia Lindner, Marlit Annalena Lipnevich, Anastasiya Mertens, Ute Meurers, Detmar Murayama, Kou Nazaretsky, Tanya Neumann, Knut Panadero, Ernesto Pankiewicz, Maciej Pardos, Zachary A. Piech, Chris Pünjer, Hannah Rummel, Nikol Steinbach, Marlene Viberg, Olga Winstone, Naomi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12463 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Future of Feedback: How Can AI Help Transform Feedback to Be More Engaging, Effective, and Scalable? Meyer, Jennifer Köller, Olaf Jansen, Thorben Fleckenstein, Johanna Asher, Michael W. Bichler, Sarah Brandl, Laura Breitwieser, Jasmin Cortina, Kai S. Cukurova, Mutlu Daumiller, Martin Deininger, Hannah Fischer, Frank Gašević, Dragan Grütter, Jeanine Hilz, Anna Jivet, Ioana Jovanović, Jelena Kizilcec, Rene F. Kuklick, Livia Lindner, Marlit Annalena Lipnevich, Anastasiya Mertens, Ute Meurers, Detmar Murayama, Kou Nazaretsky, Tanya Neumann, Knut Panadero, Ernesto Pankiewicz, Maciej Pardos, Zachary A. Piech, Chris Pünjer, Hannah Rummel, Nikol Steinbach, Marlene Viberg, Olga Winstone, Naomi Computers and Society 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. |
| title | The Future of Feedback: How Can AI Help Transform Feedback to Be More Engaging, Effective, and Scalable? |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2603.12463 |