Hybrid AI feedback prompted the most student revisions

What the study found

The study found that a hybrid form of AI-generated feedback, which combined directive and metacognitive elements, prompted the most revisions from students. Confidence ratings were high in all groups, and the quality of the completed work was comparable across feedback types.

Why the authors say this matters

The authors conclude that the findings highlight the promise of AI in giving feedback that balances clarity with reflection. They suggest that hybrid approaches may help structure AI-generated feedback to support both of these features.

What the researchers tested

The researchers ran a semester-long randomized controlled trial in an introductory design and programming course using an adaptive educational platform. They assigned 329 students to receive directive feedback, metacognitive feedback, or hybrid AI-generated feedback that blended both approaches.

Directive feedback means explicit explanations intended to reduce cognitive load, while metacognitive feedback prompts learners to reflect on their progress and build self-regulated learning skills.

What worked and what didn't

Revision behavior differed across the feedback conditions. The hybrid condition prompted the most revisions compared with the directive and metacognitive conditions.

Confidence ratings were uniformly high, and resource quality outcomes were comparable across all conditions.

What to keep in mind

The abstract notes that more work is needed to evaluate the broader impact of hybrid AI-generated feedback. No other limitations are described in the available summary.