对话式AI能提升学生独立思考能力，推动教育模式变革。
Beyond Automation: Socratic AI, Epistemic Agency, and the Implications of the Emergence of Orchestrated Multi-Agent Learning Architectures
- 用苏格拉底式对话引导学生构建研究问题，激发批判性思维。
- 65名德国师范生实验中，使用AI导师者反思能力显著更强。
- 提出可定制的多智能体学习系统，适合教育工作者设计教学流程。
生成式AI已不再是高等教育的边缘工具，正演变为重塑知识生产、传播与验证方式的通用基础设施。本研究通过一项受控实验，评估了一种基于建构主义理论的苏格拉底式AI导师（Socratic AI Tutor），该模型通过结构化对话协助预科教师学生发展研究问题。实验对象为65名德国预科教师学生，对比其与未受指导的AI聊天机器人互动的效果。结果显示，使用苏格拉底导师的学生在批判性、独立性和反思性思维支持上显著更优，表明对话式AI可促进元认知参与，挑战了生成式AI导致技能退化的主流叙事。研究进一步提出‘协调式多智能体系统’（orchestrated MAS）概念，即由教育者设计、角色分工明确、协同运作的模块化智能体组合，以支持多样化学习路径。为此，我们引入适应性‘提供与使用模型’，让学生主动采纳智能体提供的教学资源。除技术可行性外，还探讨了对高校机构与学生的系统级影响，包括资金需求、教师角色转变、课程重构及评估方式革新。最后通过成本效益分析，凸显此类系统的可扩展性。本研究既提供实证依据，也构建了融合人机共治与教学对齐的混合学习生态的概念蓝图。
原文摘要 · Abstract (English)
Generative AI is no longer a peripheral tool in higher education. It is rapidly evolving into a general-purpose infrastructure that reshapes how knowledge is generated, mediated, and validated. This paper presents findings from a controlled experiment evaluating a Socratic AI Tutor, a large language model designed to scaffold student research question development through structured dialogue grounded in constructivist theory. Conducted with 65 pre-service teacher students in Germany, the study compares interaction with the Socratic Tutor to engagement with an uninstructed AI chatbot. Students using the Socratic Tutor reported significantly greater support for critical, independent, and reflective thinking, suggesting that dialogic AI can stimulate metacognitive engagement and challenging recent narratives of de-skilling due to generative AI usage. These findings serve as a proof of concept for a broader pedagogical shift: the use of multi-agent systems (MAS) composed of specialised AI agents. To conceptualise this, we introduce the notion of orchestrated MAS, modular, pedagogically aligned agent constellations, curated by educators, that support diverse learning trajectories through differentiated roles and coordinated interaction. To anchor this shift, we propose an adapted offer-and-use model, in which students appropriate instructional offers from these agents. Beyond technical feasibility, we examine system-level implications for higher education institutions and students, including funding necessities, changes to faculty roles, curriculars, competencies and assessment practices. We conclude with a comparative cost-effectiveness analysis highlighting the scalability of such systems. In sum, this study contributes both empirical evidence and a conceptual roadmap for hybrid learning ecosystems that embed human-AI co-agency and pedagogical alignment.
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