Date
Publisher
arXiv
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.
Study at a glance
- Question: How can generative AI help make educational feedback more engaging, effective, and scalable?
- Setting and data: 50 scholars from educational psychology, computer science, science education, and the learning sciences
- Method: Meeting report synthesizing discussions from structured small-group activities
Why it matters
With digital learning environments becoming more prevalent, generative AI can produce real-time, automated feedback at scale to reshape learning and teaching. However, using generative AI for feedback brings promises, risks, and unresolved challenges for educational practice.
Key findings
- According to 50 interdisciplinary scholars, generative AI easily enables scalable, real-time feedback that holds the potential to reshape learning and teaching experiences.
- Scholars share key points of agreement regarding both the promises and the risks of using generative AI feedback in educational practice.
- Significant debates and unresolved challenges remain across disciplines regarding the use of generative AI for educational feedback.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Why use AI?
