This study adopts an integrated distributed cognition and regulation of learning perspective to examine the collaboration patterns and dynamics of human-AI collaboration when college students collaborating with AI for complex problem-solving. Through cluster analysis, three distinct collaborative problem-solving modes were identified in this study: Delegated Reasoning (DR), Concerted Interpretation (CI), and Delegated Elaboration (DE). This study found that the DR group achieved the highest task performance, significantly outperforming the CI group. Additionally, the semantic similarity between human and AI discourse was notably the highest in the DR group. In contrast, the CI group reported significantly greater use of self-regulation strategies. These findings uncover a critical tension between the efficiency of the distributed system and the depth of human learners regulatory engagement. Insights from this study offer valuable implications for the future design of AI-empowered educational tools and student-AI collaborative learning frameworks.

翻译：本研究整合分布式认知与学习调节的双重视角，考察大学生与人工智能进行复杂问题解决时的协作模式与动态机制。通过聚类分析，识别出三种不同的人机协作问题解决模式：委托推理、协同诠释与委托推敲。研究发现，委托推理组取得了最高的任务绩效，显著优于协同诠释组。此外，委托推理组中人类与AI话语之间的语义相似度最为突出，而协同诠释组报告了显著更多的自我调节策略使用。这些发现揭示了分布式系统效率与人类学习者调节参与深度之间的关键张力。本研究为未来人工智能赋能教育工具及学生-AI协作学习框架的设计提供了重要启示。