基于认知流畅性的课程思政素材生成方法OA

Large language models(LLMs)demonstrate significant potential in curriculum ideological and political education,assisting teachers in rapidly generating ideological and political education cases integrated with value guidance.However,the challenge of aligning"knowledge points with ideological and political elements"limits their application.In response to this challenge,this paper proposed a"generator-evaluator"framework based on cognitive fluency and constructed an IPEC-GE dataset comprising 839 high-quality samples.The generator produced ideological and political education cases following a structured chain:"case introduction → knowledge linkage → ideological sublimation"guided by specific prompts.Two evaluators assessed the two dimensions of cognitive fluency:logical consistency and semantic coherence.Through collaborative optimization,the generator and evaluators enhanced the quality of generated content and resolved the alignment issue between"knowledge points and ideological and political elements."Experimental results show that CF-IPG method successfully gener-ates ideological and political education cases for 100 knowledge points in Data Structure,achieving a human evaluation score of 0.902 1.Through dual-dimensional evaluation of cognitive fluency and a collaborative training mechanism,it effectively achieves deep alignment between knowledge points and ideological and political education elements,providing an efficient and feasible technical way for the large-scale application of course-based ideological and political education.