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

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Jessica Hutchison, Ian Tyler Applebaum, Kenneth Angelikas, Kush Rakesh Patel, Phuoc Nguyen, Antonio Lazaro, Nicholas Rucinski, Rahad Arman Nabid, Stephen MacNeil
• Venue: arXiv: cs.HC
• Published: 2026-06-29
• Collected: 2026-06-30
• URL: http://arxiv.org/abs/2606.30549v1
• PDF: https://arxiv.org/pdf/2606.30549v1
• DOI: 10.1145/3803400.3809394

Plain-Language Brief

Researchers have developed a new code completion tool called Clover to help students write programs using AI suggestions. The tool logs interactions with the suggestions and offers attention checks to encourage critical thinking. An analysis of relationships between interaction patterns, engagement with attention checks, and task performance shows that higher rates of accepting AI suggestions without further exploration were associated with lower scores on attention check questions, while increased dwell time (time spent looking at a suggestion) was associated with higher scores.

What the Document Says

• AI code completion tools like Github Copilot are used to help students write programs by suggesting code.
• Recent qualitative studies suggest that students may not critically evaluate these suggestions.
• The study presents Clover, a tool that logs student interactions with code suggestions and offers attention checks.
• A taxonomy of behavioral interaction metrics was developed for AI-assisted programming.
• An analysis was done on relationships between interaction patterns, engagement with attention checks, and task performance.
• Higher rates of tab accept (accepting AI suggestions without further exploration) were associated with lower scores on attention check questions.
• Increased dwell time (time spent looking at a suggestion) was associated with higher scores.

Key Concepts Mentioned

• AI code completion tools
• Github Copilot
• Clover
• students
• qualitative studies
• critical evaluation
• interaction patterns
• attention checks
• behavioral interaction metrics
• task performance
• dwell time
• tab accept

Claims Made by the Document

• Students may not critically evaluate AI code suggestions.
• Clover can help students write programs by offering attention checks and logging interactions with AI suggestions.
• Higher rates of tab accept are associated with lower scores on attention check questions.
• Increased dwell time is associated with higher scores.

Evidence Used

• Recent qualitative studies
• analysis of relationships between interaction patterns, engagement with attention checks, and task performance

Methods / Approach

• Development of a taxonomy of behavioral interaction metrics for AI-assisted programming.
• Analysis of relationships between interaction patterns, engagement with attention checks, and task performance.

Limitations Stated by the Authors

• The study only analyzed data from students, so it is unclear whether the findings apply to professional programmers.

Limitations Noted by the Agent

Agent interpretation — not from the source.

• The sample size for the analysis was not specified in the text.

Open Questions

• What are the long-term effects of using Clover on students' coding skills and critical thinking abilities?

Notes for Future Q&A

May include interpretation. Labeled where it does.

• The study suggests that higher rates of tab accept (accepting AI suggestions without further exploration) are associated with lower scores on attention check questions. However, it does not state whether this implies causation or simply a correlation.
• The study does not specify the sample size for the analysis.

Raw Abstract / Excerpt

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.