生成式教育评价:混合增强智能驱动的人机协同评价范式创新与实践OA

The paradigm of educational evaluation has continued to evolve alongside technological advancement.The rise of generative artificial intelligence is driving a shift from perceptual intelligence to cognitive intelligence,thereby creating new techno-logical opportunities for upgrading intelligent educational evaluation.However,existing human-machine collaborative evaluation ap-proaches still lack an end-to-end intelligent implementation mechanism.Hybrid-augmented intelligence based on large language models has key capabilities such as multi-source knowledge integration,complex activity understanding,and human-machine collab-orative verification.These capabilities provide technological support for theoretical innovation in educational evaluation and offer practical pathways for evaluation reform.Guided by the theory of hybrid-augmented intelligence and grounded in the value orientation of talent cultivation,this study clarifies the logical framework of generative educational evaluation and develops a generative educa-tional evaluation model supported by human-machine collaborative strategies.It further explains the technical mechanism through which generative educational evaluation becomes both"computable"and"decision-supportive,"including six structural modules:holographic data collection,multidimensional data analysis,automatic indicator discovery,automatic feedback generation,educational evaluation optimization,and trustworthy recommendation for decision-making.Based on this framework,the study explores the appli-cation of generative educational evaluation in multiple scenarios,including student academic assessment,teachers' instructional en-gagement evaluation,and university program monitoring.The findings indicate that a full-process,multi-granularity educational sys-tem of data collection and governance,prompting strategies that integrate value orientation with professional knowledge,and a multi-level validity-based collaborative verification mechanism provide critical support for translating generative educational evaluation from theory into practice.Overall,generative educational evaluation integrates the complementary strengths of human cognitive wisdom and machine-based data perception.Through holographic and multidimensional measurement of educational processes and outcomes,it can iteratively generate personalized value judgments and provide a new paradigm for educational evaluation and decision-making.