Investigating the effects of PuppyCodeReview, an AI-based code review system, on students’ cognitive load - Chun-Hsiung Tseng; Hao-Chiang Koong Lin; Andrew Chih-Wei Huang; Jia-Rou Lin (SoftwareX, 2025)
This study presents PuppyCodeReview, an AI-based system designed to support code review activities in programming education. While peer code review is increasingly emphasized in computer science curricula, its effectiveness in university classrooms remains uncertain, especially among novice programmers. PuppyCodeReview aims to address this gap by automating the review process and providing structured feedback on design, functionality, complexity, and common code smells. To evaluate the system’s impact, a four-week experiment was conducted in a data structures course at a university in Taiwan. Students were divided into control and experimental groups; the control group used a standard web interface, while the experimental group utilized the AI-assisted PuppyCodeReview system for the same tasks. Cognitive load was measured before and after the intervention. Results indicated that although cognitive load increased in both groups, the increase was significantly smaller for students using PuppyCodeReview, suggesting the system reduced mental effort associated with code review. These findings highlight the potential of AI-assisted tools in making peer review more accessible and pedagogically effective for novice programmers.