Fast and Forgettable: A Controlled Study of Novices' Performance, Learning, Workload, and Emotion in AI-Assisted and Human Pair Programming Paradigms
Nicholas Gardella, James Prather, Juho Leinonen, Paul Denny, Raymond Pettit, Sara L. Riggs

TL;DR
This study compares novice programmers' performance, workload, and emotions when using AI-assisted versus human pair programming, revealing better performance with AI but more positive emotions with humans.
Contribution
It provides empirical evidence on the effects of AI-assisted pair programming on performance, workload, and emotional experience, highlighting benefits and emotional drawbacks.
Findings
Participants performed better with GitHub Copilot than with human partners.
Workload was significantly reduced when using AI assistance.
Emotional positivity was higher with human teammates than with AI.
Abstract
Code-generating Artificial Intelligence has gained popularity within both professional and educational programming settings over the past several years. While research and pedagogy are beginning to cope with this change, computing students are left to bear the unforeseen consequences of AI amidst a dearth of empirical evidence about its effects. Though pair programming between students is well studied and known to be beneficial to self-efficacy and academic achievement, it remains underutilized and further threatened by the proposition that AI can replace a human programming partner. In this paper, we present a controlled pair programming study with 22 participants who wrote Python code under time pressure in teams of two and individually with GitHub Copilot for 20 minutes each. They were incentivized by bonus compensation to balance performance with understanding and were retested…
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