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Constructing a generative teaching capability maturity model
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AI
DOI：10.1080/10494820.2026.2724398.png)
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Abstract
En 中文
The rapid expansion of generative artificial intelligence (AI) has created a need for clearer guidance regarding the capabilities educators require and how these capabilities develop. This study constructed an initial Generative Teaching Capability Maturity Model (GTCMM), organized its capability indicators, and estimated their expert-rated priorities. A sequential expert-elicitation design combined two rounds of the Modified Delphi Method with the Fuzzy Analytic Hierarchy Process. Twenty-one experts in education, instructional design, or workplace learning with practical generative AI experience evaluated the model. The Delphi process identified 25 indicators across five maturity levels. The Defined level received the highest priority weight (0.406), followed by Quantitatively Managed (0.248), Optimized (0.139), Managed (0.135), and Initial (0.070). The framework distinguishes developmental sequence from strategic priority, recognizing that foundational capabilities remain prerequisites even when advanced practices receive greater weights. The GTCMM conceptualizes generative teaching as the progressive integration of pedagogical knowledge, professional agency, ethical judgment, assessment, and AI-supported design. As an expert-consensus-supported initial framework, it may inform professional development and institutional planning. However, classroom-based, psychometric, usability, longitudinal, and cross-cultural validation is required before it can function as a validated assessment or implementation model.
Keywords:
Generative AI
generative teaching
teaching capability indicators
Modified Delphi Method
Fuzzy Analytic Hierarchy Process
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