An Eye for AI: Eye-Tracking the Micro-Interruptions of GenAI Code Suggestions

Generative AI code suggestion tools, such as GitHub Copilot, are increasingly integrated into developer workflows. While these tools promise productivity gains, the actual impact on developer cognition and task performance has been mixed. In this paper, we present the first in-depth eye-tracking study of how developers interact with generative AI code suggestions during programming. We recruited 33 professionals and student developers and recorded their gaze behavior, code suggestion interactions, and code editing activity during programming sessions. By combining high-resolution eye-tracking data with fine-grained logging of AI-generated suggestions, we quantify the cognitive costs of reviewing code suggestions. Our findings show that approximately half of the generated suggestions are not even looked at. From the suggestions that were looked at, over 75% are not used. Although suggestions are only reviewed briefly (∼0.9 seconds on average), each suggestion introduces a micro-interruption that disrupts developer flow. We discuss opportunities for more efficient and context-aware generative AI code suggestions that minimize cognitive overhead.

• Human-centered computing → Empirical studies in HCI; Laboratory experiments; • Software and its engineering → Software creation and management.