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

The researchers developed a code completion tool called Clover that logs students' interactions with code suggestions and includes attention checks to measure critical thinking during programming tasks. The study found that higher rates of accepting the suggested code without review (tab accept) were associated with lower performance on attention checks, while increased time spent reviewing the suggested code (dwell time) was associated with better performance on attention checks.

What the Document Says

The text discusses a study that presents Clover, a code completion tool designed to log students' interactions with code suggestions and offer attention checks for reflective engagement during programming tasks.
The authors developed a taxonomy of behavioral interaction metrics for AI-assisted programming based on existing literature.
The study analyzed relationships between interaction patterns, engagement with attention checks, and task performance.
Higher rates of tab accept were found to be associated with lower attention check performance.
Increased dwell time was associated with higher attention check performance.

Key Concepts Mentioned

AI code completion tools
Clover
tab accept
dwell time
attention checks
reflective engagement

Claims Made by the Document

Higher rates of tab accept are associated with lower attention check performance.
Increased dwell time is associated with higher attention check performance.

Evidence Used

The text mentions the observation of relationships between interaction patterns, engagement with attention checks, and task performance.

Methods / Approach

The researchers analyzed interaction patterns, engagement with attention checks, and task performance in a study.

Limitations Stated by the Authors

Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

The study only analyzes interactions within the context of Clover, which may not represent all AI code completion tools.

Open Questions

What is the long-term impact of using Clover on students' programming skills and critical thinking abilities?
How can the findings be applied to other AI code completion tools?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The text does not provide specific information about the number of participants in the study, the nature of the programming tasks, or the details of the attention checks.
It is unclear whether the findings can be generalized to non-student programmers.

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.