Evaluating LLM-Generated Feedback on Handwritten Mathematics: Feedback Quality and Subsequent Exam Performance

Overview & Synopsis

We evaluate the quality of feedback and practice exercises generated by GPT-5.1 from handwritten university mathematics answers and their grades, and examine whether receiving this feedback improves subsequent exam performance. We use six weekly bonus tests in an English (n=343) and a Dutch (n=236) cohort. Tests 1 and 3 used a crossover feedback design; otherwise, all attendees received feedback. To evaluate the feedback, six reviewers assessed 157 messages from three English-cohort tasks using six criteria scored from 0 to 3. The practice exercises received the highest proportion of strong ratings (95.5%), while diagnostic specificity (41.1%) and clarity and tone (33.4%) received fewer. Feedback for the lowest-scoring answers was rated as less actionable, suggesting that these students may need more targeted guidance from instructors. Comparisons of related exam questions showed mixed results, with no statistically significant differences after adjustment across four comparisons. Greater attendance and feedback exposure were associated with higher exam scores, but the contribution of feedback could not be separated from attendance and practice. Our findings show the potential of an integrated framework merging instructor guidance with automated feedback generation to provide personalised learning support at scale.