As AI increasingly enters the classroom, what changes when students collaborate with algorithms instead of peers? We analyzed 36 undergraduate students learning graph theory through peer collaboration (n=24) or AI assistance (n=12), using discourse analysis to identify interaction patterns shaping learning outcomes. Results reveal a collaboration quality divide: high-quality peer interactions generated curiosity and engagement that AI couldn't match, yet low-quality peer interactions performed worse than AI across dimensions. AI showed a paradoxical pattern, building confidence in knowledge while reducing curiosity and deeper engagement. Interaction quality emerged from dynamic patterns rather than individual traits, with early discourse markers predicting outcomes. Students treated AI as a transactional information source despite its collaborative design, revealing fundamental differences in human versus algorithmic engagement. Our findings suggest AI in education need not replace peer learning but can recognize struggle and support both peer and AI interactions toward productive learning experiences.

翻译：随着人工智能日益进入课堂，当学生与算法而非同伴协作时，会发生什么变化？我们分析了36名本科生通过同伴协作（n=24）或人工智能辅助（n=12）学习图论的过程，运用话语分析识别影响学习结果的交互模式。研究结果揭示了协作质量的分化：高质量的同伴互动能激发人工智能无法比拟的好奇心与参与度，而低质量的同伴互动在各维度上表现均逊于人工智能。人工智能呈现出一种矛盾模式：它在建立知识自信的同时，却削弱了好奇心与深度参与。交互质量源于动态模式而非个体特质，早期话语标记可预测学习结果。尽管人工智能被设计为协作工具，学生仍将其视为交易性信息源，这揭示了人类与算法参与的根本差异。我们的研究表明，教育中的人工智能无需取代同伴学习，而应能识别学习困境，并支持同伴与人工智能交互共同导向富有成效的学习体验。