Enacted Feedback produces more post-workflow revisions per resource than the comparison workflows¶

Claim · All claims
Evidence · 1 study · causal r? · q3
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i? Model-estimated revision counts were highest in Enacted Feedback (0.602 per resource), exceeding Directed Feedback (0.239; IRR = 2.52) and Self-Directed Feedback (0.0034); no standardised effect size was printed. → Omar Alsaiari 2026

Evidence¶

Omar Alsaiari 2026¶

Omar Alsaiari, Nilufar Baghaei, Jason M. Lodge, Naomi Winstone, Dragan Gašević, Hassan Khosravi. (2026). Making AI-Generated Feedback Matter: A Large-Scale Study of Feedback Workflows and Student Enactment. https://arxiv.org/abs/2608.11625 doi:10.48550/arxiv.2608.11625

q3 · i? · causal · r?

Negative-binomial GLMM on revision counts capped at the empirical 99th percentile (maximum 8 revisions). The Enacted/Directed contrast showed IRR = 2.52, a difference of 0.363 revisions per resource, p < .001. Observed medians were 0 in all three conditions.

"Mean revision counts after capping were highest inEnacted Feedback(M= 0.87 , SD= 1.82 ), followed byDirected Feedback( M= 0.46 , SD= 1.26 ), andSelf-Directed Feedback(M= 0.01 ,SD= 0.24 )."

Discussion¶

Related Claims¶

• Enacted Feedback yields higher workflow-specific uptake of AI-generated feedback than Directed and Self-Directed Feedback workflows — related
• In the Enacted Feedback workflow most resources moved from AI feedback to suggestion selection, while optional AI assistance was rarely entered — related
• The study dataset comprised 13,037 students, 51,296 student-authored resources, and 70 course offerings across three workflow conditions — related
• Revision after chatbot feedback predominantly results in successful uptake, indicating effective error correction — related