Making AI-Generated Feedback Matter: A Large-Scale Study of Feedback Workflows and Student Enactment
Quick summary
arXiv:2608.11625v2 Announce Type: replace Abstract: Feedback processes strongly influence student learning, yet their educational value depends on addressing two distinct challenges: providing high-quality, timely, and individualised feedback at scale, and supporting students to interpret, evaluate, and act on that feedback productively. Generative AI offers a credible means of addressing the provision challenge, but students' uptake of AI-generated feedback remains limited. We conducted a large-scale quasi-experimental sequential cohort study comparing three AI-mediated feedback workflows acr
Key takeaways
- arXiv:2608.11625v2 Announce Type: replace Abstract: Feedback processes strongly influence student learning, yet their educational value depends on addressing two distinct challenges: providing high-quality, timely, and individualised feedback at scale, and supporting students to interpret, evaluate, and act on that feedback productively.
- Generative AI offers a credible means of addressing the provision challenge, but students' uptake of AI-generated feedback remains limited.
- We conducted a large-scale quasi-experimental sequential cohort study comparing three AI-mediated feedback workflows acr
Why it matters
“Making AI-Generated Feedback Matter: A Large-Scale Study of Feedback Workflows and Student Enactment” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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