人工智能科学(AI for science,AI4S)为代表的新范式正在重塑科学研究.作为AI4S的关键技术,大语言模型在教育智能体建模与仿真、教育过程挖掘、教育数据增广等方面展现出了巨大潜力.研究立足科学哲学领域的"问题-方法-过程"框架,剖析了大语言模型引发的教育研究范式变革图景:在问题维度,大语言模型基于海量数据形成的"世界知识"拓宽了教育研究的问题视野;在方法维度,大语言模型依托其"泛思维链"能力,为情境建模、模拟仿真、因果推断等方法创新提供新的可能;在过程维度;大语言模型为"端到端"和"人在回路"理念在教育研究中的融合提供了理想的技术载体,开启了人机协同的新范式;结合教育研究范式演进的历史维度,当前AI4S引领的变革是社会需求牵引和技术进步双重驱动的必然,既延续了数字时代教育研究范式的演进逻辑,还在智能维度、生成式范式、跨界整合等方面实现了独特突破.需要指出的是,这场范式变革虽然前景广阔,但其复杂性也不容忽视.研究对教育知识生产"单一文化"、理解错觉加剧、模型黑箱效应等潜在风险作了深度探讨,提出 了重塑教育研究的反思性、审慎评估大语言模型适用边界的策略实施,为应对AI4S时代的教育机遇与挑战提供了新思路.
The Paradigm Shift Induced by AI4S:Problems,Methods,and Processes of Educational Research from the Perspective of Large Language Models
An emerging paradigm,represented by AI for Science(AI4S),is reshaping scientific research.As a core technology within AI4S,large language models(LLMs)have demonstrated significant potential in education-related agent modeling and simula-tion,educational process mining,and data augmentation.This study,based on the"problem-method-process"framework of the phi-losophy of science,analyzes the paradigm shift in educational research driven by LLMs.In terms of research problems,LLMs broaden the scope of educational inquiries by drawing from massive data to form"world knowledge".Regarding research methods,LLMs lever-age their"x-of-thought"capabilities,opening up new possibilities for situational modeling,simulation,and causal inference.In re-search processes,LLMs act as technical facilitators that integrates"end-to-end"and"human-in-the-loop"approaches,thus initiat-ing a new paradigm of human-machine collaboration in educational research.Considering the historical evolution of educational re-search paradigms,the transformation led by AI4S is driven by both social demand and technological advancements.It not only contin-ues the evolutionary trajectory of educational research in the digital era but also achieves distinct breakthroughs in intelligence,gen-erative paradigms,and cross-disciplinary integration.However,despite its promising prospects,the complexity of this paradigm shift cannot be overlooked.The study discusses potential risks,including the"monoculture"of educational knowledge production,the illu-sion of understanding,and the black-box nature of models.It proposes strategies to enhance the reflexivity in educational research and to critically evaluate the applicability and limitations of LLMs,offering new approaches to navigate the opportunities and chal-lenges of the AI4S era.
AI for ScienceLarge Language ModelsEducational Research ParadigmWorld KnowledgeChain of ThoughtHuman-in-the-Loop
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