AI-Assisted Code Review as a Scaffold for Code Quality and Self-Regulated Learning: An Experience Report
Code review is central to software engineering education but hard to scale in capstone projects due to tight deadlines, uneven peer feedback, and limited prior experience. We investigate an LLM-as-reviewer integrated directly into GitHub pull requests (human-in-the-loop) across two cohorts (more than 100 students, 2023--2024). Using a mixed-methods design -- GitHub data, reflective reports, and a targeted survey -- we examine engagement and responsiveness as behavioral indicators of self-regulated learning processes. Quantitatively, the 2024 cohort produced more iterative activity (1176 vs. 581 PRs), while technical issues observed in 2023 (227 failed AI attempts) dropped to zero after to...
| Authors | Eduardo Oliveira, Michael Fu, Patanamon Thongtanunam et al. |
|---|---|
| Primary category | cs.SE |
| Published | 25 April 2026 |
| Last updated at source | 25 April 2026 |
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