To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checks
arXiv:2606.30549v1 Announce Type: cross 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 relati
The proliferation of AI code completion tools in education and professional settings necessitates a deeper understanding of their impact on critical thinking and skill development.
Understanding how users interact with and critically evaluate AI-generated code is crucial for educational outcomes, software quality, and the effective integration of AI into development workflows.
The focus expands from mere code generation efficiency to the cognitive impact and critical engagement required when using AI assistance in programming, potentially leading to new design paradigms for AI tools.
- · AI education platforms
- · Developers of critical AI tools
- · Cybersecurity training
- · Students lacking critical evaluation skills
- · Companies uncritically adopting AI code
- · Traditional rote-learning programming education
Metrics for evaluating the quality and criticality of AI-assisted code become standardized.
Educational curricula will adapt to explicitly teach critical evaluation of AI-generated content in programming.
The development of AI tools shifts towards 'co-pilots' that actively prompt users for critical engagement rather than passive completion.
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Read at arXiv cs.AI