远程教育杂志2026，Vol.44Issue(2)：53-62,10.DOI:10.15881/j.cnki.cn33-1304/g4.2026.02.006
教育大模型赋能隐性知识转化:价值意蕴、赋能逻辑与实践进路
Empowering Tacit Knowledge Transformation by Educational Large Models:Value Implications,Enabling Logic,and Practical Pathways
摘要
Abstract
Tacit knowledge,as a core resource underpinning teachers' professional judgment,classroom management,and con-textual sensitivity,has long been constrained by difficulties in its capture,transformation,and transfer.With the rapid advancement of generative artificial intelligence,educational large models,driven by large language models,multimodal systems,and semantic knowl-edge graph technologies,have opened up new possibilities for addressing these challenges.Grounded in the SECI knowledge conver-sion model,this study focuses on three key technological affordances of educational large models,which are multimodal integration,contextual reconstruction,and cognitive augmentation,and proposes an enabling logic through which they can facilitate tacit knowl-edge transformation.Furthermore,this study proposes a five-layer operational architecture for educational large models oriented to-ward tacit knowledge transformation,comprising the physical layer,data layer,analysis layer,interface layer,and service layer.This architecture provides technical support for knowledge transformation,which follows a pathway of perception-articulation-modeling-generation.It enables tacit knowledge to shift from inexpressible forms toward preliminary states of articulation and perceptibility.Ed-ucational large models are thus evolving from tools for content generation into cognitive partners in teaching,offering promising ways to address challenges of structural representation and contextual transfer of tacit knowledge.关键词
教育大模型/隐性知识/SECI模型/知识转化/生成式人工智能Key words
Educational large models/Tacit knowledge/SECI model/Knowledge transformation/Generative artificial intelligence分类
社会科学引用本文复制引用
罗江华,付传..教育大模型赋能隐性知识转化:价值意蕴、赋能逻辑与实践进路[J].远程教育杂志,2026,44(2):53-62,10.基金项目
2025年国家自然科学基金面上项目"生成式人工智能增强学科教学适应性的人机协同机理与多模态反馈机制研究"(项目编号:62577046). （项目编号:62577046）
