Students' Perception Accuracy of Partners' AI Use and its Relation to Collaboration Performance
2026-06-22 • Human-Computer Interaction
Human-Computer InteractionComputers and Society
AI summaryⓘ
The authors studied how well students working in pairs on programming projects understand each other's use of AI tools. They found that when students have different ideas about how much their partner used AI early on, their project scores tend to be lower. This problem is worse for students who already have less programming experience. The study also showed that working together in person doesn't always fix these misunderstandings, so the authors suggest that teams might need better ways to be open about their AI use.
collaborative programminggenerative AIpair programmingperception alignmentteam performancesoftware engineering educationlongitudinal studyAI use transparencyprogramming educationmisaligned perceptions
Authors
Laura Graf, Ramona Beinstingel, Stephan Kusche, Oleksandra Poquet
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.