"AI or Ally? Understanding Student Reliance on GitHub Copilot and Human Peers Through Eye Tracking"

"As AI-powered tools like GitHub Copilot become increasingly integrated into development workflows, it\u2019s essential to understand how developers\u2014particularly novices\u2014interact with these systems compared to traditional peer collaboration. This paper presents a within-subjects eye-tracking study of novice developers completing programming tasks with assistance from either GitHub Copilot or a human peer.We evaluate participants\u2019 task performance, visual attention patterns, and perceived reliance on the assistant. Results show that participants achieved significantly higher correctness scores with Copilot (50\\%) than with peers (32\\%), but also spent more time and exhibited higher cognitive load. Participants relied more frequently on Copilot, spending more time fixating on the AI interface, even though they visited the peer chat pane more frequently than Copilot\u2019s. Moreover, subjective responses indicated that Copilot was widely credited with enhancing speed and concentration, whereas opinions on its impact on code quality were more mixed. Visual attention patterns further indicated assistant-specific strategies, with significant differences in how participants navigated code panes, project structures, and error traces depending on the support type.These findings highlight important trade-offs between AI and human support in programming workflows and offer design implications for future intelligent development environments that better balance correctness, trust, and cognitive effort."