[2606.30549] To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checks

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Computer Science > Human-Computer Interaction

arXiv:2606.30549 (cs)

[Submitted on 29 Jun 2026]

Title:To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checks

Authors:Jessica Hutchison, Ian Tyler Applebaum, Kenneth Angelikas, Kush Rakesh Patel, Phuoc Nguyen, Antonio Lazaro, Nicholas Rucinski, Rahad Arman Nabid, Stephen MacNeil<br>View a PDF of the paper titled To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checks, by Jessica Hutchison and 8 other authors

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Abstract:AI code completion tools, such as Github Copilot, provide students with code suggestions to help them write programs. However, recent qualitative studies suggest that students fail to critically evaluate these suggestions. We present Clover, a code completion tool that logs students' interactions with code suggestions and additionally offers attention checks to probe reflective engagement during programming tasks. We also develop a taxonomy of behavioral interaction metrics for AI-assisted programming, informed by literature. We analyzed relationships between interaction patterns, engagement with attention checks, and task performance. We observed that higher rates of tab accept were associated with lower attention check performance, while increased dwell time was associated with higher attention check performance. We conclude by discussing how programming process data and attention checks might support reflective engagement in AI-assisted programming.

Comments:<br>7 pages. Accepted for publication in the Proceedings of the 31st ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE 2026), Madrid, Spain, July 10-15, 2026. Author's accepted manuscript

Subjects:

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)

Cite as:<br>arXiv:2606.30549 [cs.HC]

(or<br>arXiv:2606.30549v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2606.30549

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arXiv-issued DOI via DataCite

Journal reference:<br>Proceedings of the 31st ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE 2026), Madrid, Spain, July 10-15, 2026

Related DOI:

https://doi.org/10.1145/3803400.3809394

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DOI(s) linking to related resources

Submission history<br>From: Ian Applebaum [view email]<br>[v1]<br>Mon, 29 Jun 2026 16:47:46 UTC (253 KB)

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