Structured workflows are key to making AI-generated feedback genuinely useful for students, leading to higher engagement and better learning outcomes.

In plain terms

Providing timely and personalized feedback is crucial for student learning, but it's hard to do at scale. While generative AI can help create feedback comments, students often don't use them effectively. This study investigated how different ways of delivering AI feedback—called 'workflows'—impact student engagement and learning. They compared three approaches across over 13,000 students: simply receiving AI comments, opting to get AI feedback, or being actively prompted to select, evaluate, and discuss AI suggestions. The researchers found that when students were guided to actively interact with and reflect on the AI feedback (the 'Enacted Feedback' workflow), they were significantly more likely to use it. This active engagement also led to higher student confidence in their self-assessments and better quality in their submitted work, showing that the design of the AI interaction is as important as the feedback quality itself.