Date
Publisher
arXiv
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.
Study at a glance
- Question: How accurately do student partners perceive each other's AI use, and does perception misalignment relate to team performance?
- Setting and data: Introductory programming course at a European university, 103 student pairs (non-CS majors) during a one-month project.
- Method: Three-wave longitudinal survey study using robust linear regression (Huber M-estimation) and correlational analyses.
Why it matters
Generative AI use is largely invisible in remote collaborative programming, weakening observable cues. When students misjudge their partners' AI reliance, coordination can be disrupted, yet empirical work on team partners' awareness of AI use remains sparse.
Key findings
- Greater early misalignment between partners' beliefs about each other's AI use was associated with lower final project scores.
- The negative association between early perception misalignment and project scores was substantially stronger for student pairs with lower prior programming performance.
- Perceptions of partner AI use were largely driven by assumed similarity rather than genuine partner-specific knowledge, and accuracy did not consistently improve over time.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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Study design
