The Future Of Feedback: How Can AI Help Transform Feedback To Be More Engaging, Effective, And Scalable?

Authors

Jennifer Meyer,

Olaf Kuller,

Thorben Jansen,

Johanna Fleckenstein,

Michael W. Asher,

Sarah Bichler,

Laura Brandl,

Jasmin Breitwieser,

Kai S. Cortina,

Mutlu Cukurova,

Martin Daumiller,

Hannah Deininger,

Frank Fischer,

Dragan Gasevic,

Jeanine Grutter,

Anna Hilz,

Ioana Jivet,

Jelena Jovanovic,

Rene F. Kizilcec,

Livia Kuklick,

Marlit Annalena Lindner,

Anastasiya Lipnevich,

Ute Mertens,

Detmar Meurers,

Kou Murayama,

Tanya Nazaretsky,

Knut Neumann,

Ernesto Panadero,

Maciej Pankiewicz,

Zachary A. Pardos,

Chris Piech,

Hannah Punjer,

Nikol Rummel,

Marlene Steinbach,

Olga Viberg,

Naomi Winstone

Date

03/2026

Publisher

arXiv

Link

https://arxiv.org/abs/2603.12463v1

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.

What is the application?

Teaching – Assessment and Feedback,

Learning – Student Support

Who is the user?

Student,

Who age?

Why use AI?