Using Biometrics to Understand AI-Assisted Coding Performance and its Perception
AI-based code assistants are transforming software development, yet we lack empirical evidence on how they affect developers' cognitive processes. We present a multisite study investigating the neurophysiological correlates of AI-assisted programming through a within-subjects crossover design. We recruited participants at two universities (Bari, Italy, and Copenhagen, Denmark) and collected electroencephalography, eye-tracking, electrodermal activity, and heart rate variability data alongside a rubric-based performance score and self-reported workload across six dimensions using the NASA Task Load Index (NASA-TLX). We tested four hypotheses addressing physiological differences between AI-...
| Authors | Paolo Burelli, Fabio Calefato, Daniela Grassi et al. |
|---|---|
| Primary category | cs.HC |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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