Eye-tracking experiment finds that labeling code as LLM-generated increases fixation time without changing review thoroughness, with reviewers adapting criteria or using the prompt.
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A multisite biometric study finds lower cognitive engagement under AI assistance via EEG and blink rate, with physiological-performance links present only in the non-AI condition.
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Same Scrutiny, More Time: Eye Tracking Insights into Reviewing LLM-Labelled Code
Eye-tracking experiment finds that labeling code as LLM-generated increases fixation time without changing review thoroughness, with reviewers adapting criteria or using the prompt.
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Using Biometrics to Understand AI-Assisted Coding Performance and its Perception
A multisite biometric study finds lower cognitive engagement under AI assistance via EEG and blink rate, with physiological-performance links present only in the non-AI condition.