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

2026-06-30 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, quick

Summary

A study introduces Clover, an AI code completion tool designed to measure students' critical engagement with code suggestions, addressing concerns that students often fail to critically evaluate tools like GitHub Copilot. Researchers developed a taxonomy of behavioral interaction metrics and integrated attention checks into Clover. Analysis revealed that higher rates of "tab accept" for suggestions correlated with lower performance on attention checks, indicating reduced critical evaluation. Conversely, increased "dwell time" on suggestions was associated with higher attention check performance. This work, accepted for the 31st ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE 2026) in Madrid, Spain, July 10-15, 2026, suggests programming process data and attention checks can foster reflective engagement in AI-assisted programming environments.

Key takeaway

For AI educators or developers building code completion tools, understanding how students interact with AI suggestions is crucial for fostering learning. You should consider integrating behavioral logging and attention checks into your tools to objectively measure critical engagement. Monitoring metrics like "tab accept" rates and "dwell time" can provide insights into whether users are reflectively evaluating suggestions or passively accepting them, guiding improvements for more effective AI-assisted learning environments.

Key insights

Behavioral signals and attention checks can effectively measure critical engagement with AI code completion suggestions.

Principles

• High tab acceptance correlates with low critical evaluation.
• Increased dwell time indicates higher critical engagement.

Method

Clover logs student interactions and uses embedded attention checks to probe reflective engagement during AI-assisted programming tasks.

In practice

• Integrate attention checks into AI code completion tools.
• Monitor tab accept rates and dwell time as proxies for engagement.

Topics

• AI Code Completion
• Critical Engagement
• Behavioral Signals
• Attention Checks
• Human-Computer Interaction
• Software Engineering Education

Best for: AI Scientist, Research Scientist, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.