Human-Human-AI Triadic Programming: Uncovering the Role of AI Agent and the Value of Human Partner in Collaborative Learning

• URL: http://arxiv.org/abs/2601.12134v1
• Date: Sat, 17 Jan 2026 18:32:54 GMT
• Title: Human-Human-AI Triadic Programming: Uncovering the Role of AI Agent and the Value of Human Partner in Collaborative Learning
• Authors: Taufiq Daryanto, Xiaohan Ding, Kaike Ping, Lance T. Wilhelm, Yan Chen, Chris Brown, Eugenia H. Rho,
• Abstract summary: Our work introduces human-human-AI (HHAI) triadic programming, where an AI agent serves as an additional collaborator rather than a substitute for a human partner.<n>In the triadic HHAI conditions, participants relied significantly less on AI-generated code in their work.<n>These findings demonstrate how triadic settings activate socially shared regulation of learning by making AI use visible and accountable to a human peer.
• Score: 10.772613370888516
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As AI assistance becomes embedded in programming practice, researchers have increasingly examined how these systems help learners generate code and work more efficiently. However, these studies often position AI as a replacement for human collaboration and overlook the social and learning-oriented aspects that emerge in collaborative programming. Our work introduces human-human-AI (HHAI) triadic programming, where an AI agent serves as an additional collaborator rather than a substitute for a human partner. Through a within-subjects study with 20 participants, we show that triadic collaboration enhances collaborative learning and social presence compared to the dyadic human-AI (HAI) baseline. In the triadic HHAI conditions, participants relied significantly less on AI-generated code in their work. This effect was strongest in the HHAI-shared condition, where participants had an increased sense of responsibility to understand AI suggestions before applying them. These findings demonstrate how triadic settings activate socially shared regulation of learning by making AI use visible and accountable to a human peer, suggesting that AI systems that augment rather than automate peer collaboration can better preserve the learning processes that collaborative programming relies on.

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