Key result
AI-assisted coding increases negative affect, reduces parasympathetic activity, and lowers creative performance in novice programmers.
Why the study?
AI code generators are increasingly used in creative contexts, offering operational efficiencies but prompting concerns about psychological and neurophysiological strain.
Does AI-assisted coding alter emotional states, heart rate variability, and creative output in novice undergraduate design students?
RCT (n=58)
randomly assigned
Does AI-assisted coding alter emotional states, heart rate variability, and creative output in novice undergraduate design students?
p-value: p=0.006
In novice programmers, AI assistance may disrupt emotional regulation and autonomic balance, leading to reduced creative performance and self-efficacy.
AI-assisted coding may increase emotional strain and reduce creative output in novices; leaves open effects with training or expertise.
AI code generators are increasingly used in creative contexts, offering operational efficiencies on the one hand and prompting concerns about psychological and neurophysiological strain on the other. This study employed a multimodal approach to examine the affective, autonomic, and creative consequences of AI-assisted coding in early-stage learners. Fifty-eight undergraduate design students with no formal programming experience were randomly assigned to either an AI-assisted group or a control group and engaged in a two-day generative programming task. Emotional states (PANAS), creative self-efficacy (CSES), and subjective workload (NASA-TLX) were assessed, alongside continuous monitoring of heart rate variability (HRV; RMSSD and LF/HF). Compared to the controls, the AI-assisted group exhibited greater increases in negative affect (p = 0.006), reduced parasympathetic activity during the task (p = 0.001), and significant post-task declines in creative self-efficacy (p < 0.05). Expert evaluation of creative outputs revealed a significantly lower performance in the AI group (p = 0.040), corroborated by behavioral observations showing higher tool dependency, emotional volatility, and rigid problem-solving strategies. These findings indicate that, in novice users, the opacity and unpredictability of AI feedback may disrupt emotional regulation and autonomic balance, thereby undermining creative engagement. The results highlight the need to consider neurocognitive vulnerability and the learner’s developmental stage when integrating AI tools into cognitively demanding creative workflows.
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Wang et al. (2025) conducted an RCT in no formal programming experience (n=58). AI-assisted coding vs. control group was evaluated on negative affect (p=0.006). AI-assisted coding in novice programmers significantly increased negative affect (p=0.006), reduced parasympathetic activity (p=0.001), and lowered creative performance (p=0.040) compared to controls.
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