Students' Perception Accuracy of Partners' AI Use and its Relation to Collaboration Performance

2026-06-22

AI Summary

This study investigates how students’ accuracy in perceiving their partners’ use of generative AI tools during collaborative programming influences team performance. Drawing on a three-wave longitudinal dataset from 103 student dyads enrolled in a software engineering course—integrating survey responses, project grades, and observational data on pair programming practices—the research reveals, for the first time, that misperceptions regarding a partner’s AI usage negatively impact collaborative outcomes: greater early-stage perceptual bias correlates with lower final project scores. Notably, face-to-face pair programming fails to mitigate this effect. The findings further indicate that teams with lower programming proficiency are disproportionately affected by such perceptual inaccuracies, underscoring the critical need to enhance perceptual accuracy in AI-augmented collaborative learning environments.

Abstract

Collaborative assignments are a cornerstone of programming education. Effective collaboration during a programming project depends on the formation of reasonably accurate beliefs about how each partner works. Generative AI tools, now widely used by undergraduate students, have introduced a consequential and largely invisible new dimension into collaboration: each student's use of AI. When partners collaborate remotely, they interpret partners' ability and effort through their code. This raises the question of how accurately students perceive each other's AI use in collaborations, and if a misalignment in these perceptions relates to team performance. To address this question, we conducted a three-wave longitudinal study of 103 student pairs in an introductory software engineering course. We found that greater misalignment between partners' beliefs about each other's AI use early in the project was associated with lower final project scores. The effect of such misaligned perceptions is the strongest in teams with lower prior programming performance, suggesting that low performing students pay a higher cost of misaligned perceptions. The perception misalignment does not consistently decrease through face-to-face pair-programming sessions. This suggests that ways to foster transparency may be needed to support student teams in collaborative programming.

Problem

• AI use perception
• collaboration performance
• student collaboration
• generative AI
• perception alignment

Innovation

• perception accuracy
• AI use transparency
• collaborative programming
• generative AI
• team performance