AI-supported co-produced feedback achieved comparable quality and statistically significant learning gains with reduced production time and workload¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? In one reviewed study, feedback co-produced by teachers and AI showed quality comparable to manual feedback and statistically significant learning gains, with significant reductions in feedback production time and perceived workload. → Mutlu Cukurova 2025

Evidence¶

Mutlu Cukurova 2025¶

Mutlu Cukurova, Wannapon Suraworachet, Qi Zhou, and Sahan Bulathwela. (2025). Towards Synergistic Teacher-AI Interactions with Generative Artificial Intelligence. https://arxiv.org/abs/2511.19580 doi:10.48550/arxiv.2511.19580

q2 · i? · review · r2

The review reports this secondhand, citing Reza et al.'s study of AI praxically supporting teachers in producing feedback for students. The chapter also notes participants complained about AI randomness and reengineering prompts to shape pedagogically aligned results.

"The co-produced feedback reveals comparable quality to manual feedback and statistically significant learning gains, yet with a significant reduction of feedback production time and perceived workload."

Discussion¶

Related Claims¶

• Using the AIED-U system yielded a positive indirect effect on learning gains through a reduction in the effort teachers reported — related
• Interest in AI for feedback in higher education is driven by workload reduction and feedback timeliness amid massification — related
• Praxical teacher-AI grading teaming decreased grading time by 44% and increased accuracy by 6% versus manual grading — related
• A technical review reports a 70% cut in grading time and a 15% rise in learner satisfaction from NLP-based automated grading on one MOOC platform, with no comparison design described — related
• NLP-based feedback generation for textual student answers in large courses reduced grading effort by up to 85% with high precision (reported from Bernius et al.) — related
• Teacher perceived workload did not differ significantly across conditions on any NASA-TLX subscale, though the experimental condition was descriptively lower on most subscales — reports the opposite