AI-Assisted Code Review as a Scaffold for Code Quality and Self-Regulated Learning: An Experience Report

📰 ArXiv cs.AI

Learn how AI-assisted code review can improve code quality and self-regulated learning in software engineering education

intermediate Published 28 Apr 2026

Action Steps

Implement AI-assisted code review in GitHub pull requests using LLMs
Analyze engagement and response data from GitHub to evaluate effectiveness
Conduct surveys and gather reflective reports to assess student learning outcomes
Compare traditional peer review with AI-assisted code review to identify benefits and limitations
Integrate AI-assisted code review into existing software engineering curricula

Who Needs to Know This

Software engineering educators and students can benefit from AI-assisted code review to improve code quality and learning outcomes

Key Insight

💡 AI-assisted code review can enhance code quality and promote self-regulated learning in software engineering education

Key Takeaways

Learn how AI-assisted code review can improve code quality and self-regulated learning in software engineering education

Full Article

Title: AI-Assisted Code Review as a Scaffold for Code Quality and Self-Regulated Learning: An Experience Report

Abstract:
arXiv:2604.23251v1 Announce Type: cross Abstract: 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 respo

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