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

Hutchison et al. (2026) develop and validate a method for measuring critical engagement with AI code completion tools in educational settings. Using behavioral signals (time-to-accept, edit distance from suggestion) and embedded attention checks, they find that the majority of students accept AI code suggestions passively, without critically evaluating correctness or appropriateness. This 'tab-and-go' behavior directly threatens the development of programming skills, as students bypass the cognitive effort required for Transfer Of Learning. The work provides a methodological toolkit for Formative Assessment in AI-augmented programming courses, enabling instructors to detect when students are over-reliant on AI. The findings connect to Student Experience research in STEM Education by showing that the mere availability of AI tools does not lead to productive learning — structured pedagogical interventions are required to ensure students engage critically rather than deferring to AI output.

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Jessica Hutchison, Ian Tyler Applebaum, Kenneth Angelikas, Kush Rakesh Patel, Phuoc Nguyen, Antonio Lazaro, Nicholas Rucinski, Rahad Arman Nabid, Stephen MacNeil (2026). To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checks. arXiv:2606.30549. cs.HC (ITiCSE 2026).