This study addresses critical challenges in teacher artificial intelligence (AI) literacy diagnosis and educational agent matching within China’s “AI+Education” strategic context. Drawing on constructivist learning, adaptive systems, tag-based matching, and human-AI collaboration theories, it employs the teacher artificial intelligence competence self-efficacy (TAICS) scale as a standardized diagnostic instrument. Based on six-dimensional assessment data, the research establishes a four-dimensional teacher profile tagging system and a three-dimensional functional tagging system for educational agents. A “fuzzy rule-based reasoning + weighted cosine similarity” bidirectional matching algorithm is designed within a five-layer multi-agent architecture, forming a closed-loop “diagnosis – matching – scheduling – collaboration – iteration” paradigm. This data-driven model elevates teacher differences from demographic variables to system design variables, offering a computable theoretical foundation for human-AI collaborative education and practical solutions for regional precision empowerment.
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Share and Cite
Zhou X, Chang Y, Lei J, Ma Y. (2026) Research on Bidirectional Matching of Educational Agents and Human-AI Collaborative Empowerment Theories for Teachers of All Educational Stages in Zhejiang Province. Global Education Bulletin, 3(2), 116-130. https://doi.org/10.71052/grb2025/MYJI9339