科技教育大模型构建机理与实践进路OA

The integrated strategy of education,science and technology,and talent has imposed systemic requirements for transforming the cultivation of scientific and technological talent,from knowledge transmission to paradigm innovation.Generative arti-ficial intelligence provides a key driving force for this transformation.However,an inherent epistemological tension exists between its general probabilistic generation mechanisms and the empirical rigor emphasized in science and technology education.From the strate-gic perspective of the"trinity"and following the isomorphic logic of"technology(knowledge)—education(method)—talent(thinking),"this study systematically examines large language models for science and technology education,delineating its core connotations and establishing a characteristic framework.On this basis,it elucidates the internal mechanisms for developing specialized models through multimodal interdisciplinary knowledge-graph mapping,the integration of heuristic instructional strategies,and alignment with cogni-tive laws.Furthermore,it proposes practical pathways across three dimensions-field empowerment,action coordination,and iterative feedback-to promote paradigm shift in talent cultivation through virtual-real integrated environments,human-machine collaborative teaching,and evidence-based competency evaluation.This study provides a theoretical framework and practical guidance for building a digital-intelligence educational ecosystem that integrates science and education while strengthening the foundation for talent devel-opment.