用生理数据揭示AI编程助手如何改变开发者认知状态。
Using Biometrics to Understand AI-Assisted Coding Performance and its Perception
- 通过脑电、眼动等生物信号，对比AI辅助与手动编程的生理差异。
- 使用AI时大脑θ/α比值下降，眨眼频率上升，表明认知负荷降低。
- 适合关注AI辅助开发体验、人机交互设计的研究者和开发者。
基于AI的代码助手正在重塑软件开发，但我们缺乏其对开发者认知过程影响的实证证据。本研究采用跨站点、配对设计，在意大利巴里大学和丹麦哥本哈根大学招募参与者，收集脑电图（EEG）、眼动追踪、皮肤电活动和心率变异性数据，并结合评分标准性能分及六维度的主观工作量评估（NASA-TLX）。测试了四个假设：AI辅助与非辅助条件下的生理差异、开发者经验的调节作用、生理与表现的关系，以及主观感知与客观指标的一致性。结果显示，在使用AI时，首次任务中脑电θ/α比值降低，第二次任务中注视眨眼频率升高，均表明生成性任务被模型承担后认知投入减少。该模式在本科生与研究生间无显著差异。非AI条件下，皮肤电活动与表现相关，而AI条件下不相关；仅在非AI条件下，物理需求维度与表现相关。这些发现表明，AI辅助编程并非更快的独立编码，而是一种认知上截然不同的活动，对AI助手设计和人机协同开发中的生物监测具有启示意义。
原文摘要 · Abstract (English)
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-assisted and non-assisted conditions, the moderating role of developer experience, the association between physiology and performance, and the alignment between subjective perceptions and objective measures. Under AI assistance, the EEG $θ/α$ ratio was lower during the first task and the gaze blink rate was higher during the second, both consistent with reduced cognitive engagement when developers offload generative effort to the model. This pattern did not differ between undergraduate and graduate students. Electrodermal activity correlated with performance under the non-AI condition but not under AI. Among the six NASA-TLX dimensions of self-reported workload, only Physical demand was associated with performance under the non-AI condition but not under AI. These findings suggest that AI-assisted programming is not a faster version of solo coding but a cognitively distinct activity, with implications for the design of AI assistants and for biometric monitoring in AI-augmented development.
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