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

2026-03-12

AI Summary

This study addresses the dual challenges of quality and scalability in educational feedback within digital learning environments by convening 50 scholars from educational psychology, computer science, science education, and learning sciences. For the first time, it systematically integrates multidisciplinary perspectives through structured deliberations and cross-disciplinary analysis to examine the potential, risks, and implementation pathways of generative artificial intelligence (GenAI) in educational feedback. The research identifies key areas of consensus, central controversies, and critical gaps in the literature, clarifying core opportunities and challenges associated with AI-driven feedback. Building on these insights, the study proposes future research directions and practical recommendations to inform the development of effective, scalable intelligent feedback systems, thereby establishing a foundational theoretical and empirical basis for advancing this emerging field.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• generative AI
• feedback
• education
• scalability
• engagement

Innovation

• generative AI
• automated feedback
• scalable learning
• interdisciplinary synthesis
• real-time feedback