A Risk-Adaptive and Evidence-Constrained Framework for Generative AI Feedback in Programming Education
Generative artificial intelligence can turn learning analytics into personalized support, but feedback systems must decide when to intervene, which evidence to use, and how much assistance to provide. We developed a risk-adaptive, evidence-constrained framework for introductory programming using 2993 failed-submission states from 215 students. Student-disjoint models predicted persistent failure and related outcomes; four matched feedback conditions were generated for 136 cases; and calibrated risk informed capacity-limited intervention policies. The validation-selected logistic regression model achieved a test precision-recall area under the curve of 0.550 and a receiver operating charac...
| Authors | Shihao Wang |
|---|---|
| Primary category | cs.AI |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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