The Future of Feedback: How Can AI Help Transform Feedback to Be More Engaging, Effective, and Scalable?

TL;DR

This paper explores how generative AI can transform feedback in digital learning environments to make it more engaging, effective, and scalable, by synthesizing interdisciplinary perspectives and identifying future research directions.

Contribution

It provides a comprehensive synthesis of interdisciplinary views on AI-driven feedback, highlighting promises, risks, debates, and unresolved challenges in educational practice.

Findings

AI enables scalable, real-time automated feedback.

Disciplinary perspectives reveal both opportunities and concerns.

Open questions guide future research in AI-enhanced feedback.

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.