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.

翻译：随着AI辅助技术日益融入编程实践，研究者们越来越多地关注这些系统如何帮助学习者生成代码并提升工作效率。然而，现有研究往往将AI定位为人类协作的替代品，忽视了协作编程中涌现的社会性及学习导向特征。本研究提出人-人-智能体三元编程范式，将AI代理定位为人类伙伴的补充协作者而非替代者。通过一项包含20名参与者的组内实验，我们发现相较于人-智能体二元基准组，三元协作能显著提升协作学习效果与社会临场感。在三元协作条件下，参与者对AI生成代码的依赖度显著降低，这种效应在"共享式"三元协作情境中尤为突出——参与者更倾向于在应用AI建议前主动理解其含义。这些发现表明，三元协作环境通过使AI使用行为对同伴可见且可问责，能够激活社会共享的学习调节机制。这提示我们，那些旨在增强而非自动化同伴协作的AI系统，更能有效保留协作编程所依赖的学习过程。