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<p>Artificial Intelligence (AI) has sparked a revolution in education, but understanding the factors influencing STEM teachers’ intentions to use AI for educational innovation remains a challenge. This study integrates the Task–Technology Fit (TTF) model and the Technology Acceptance Model (TAM) to explore how AI technologies influence STEM teachers’ willingness to engage in innovative teaching practices. Using a combination of Partial Least Squares Structural Equation Modeling (PLE-SEM) and Fuzzy-Set Qualitative Comparative Analysis (fsQCA), empirical analysis is conducted to examine the influence pathways and antecedent configurations. The study finds that Task–Technology Fit and Attitude toward Using Teaching Innovation are key factors influencing the willingness to innovate in teaching. While the effect of Perceived Ease of Use is relatively small, it still serves as a necessary precondition for optimizing teachers’ technological experience. The fsQCA reveals three pathways that promote STEM teachers’ use of AI for teaching innovation: technology–task fit-driven, technology attraction-driven, and hybrid-driven. The results indicate that adequate technical support systems and the adaptability of AI tools to teaching innovation requirements constitute essential prerequisites for promoting AI integration in STEM education. This study provides a theoretical foundation and methodological frameworks to guide AI integration in education, addressing the practical demands of educational administrators and technology developers in crafting effective AI implementation strategies for the sector.</p>

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Task/Technology Fit or Technology Attraction? The Intentions of STEM Teachers to Use AI Technologies for Teaching Innovation

  • 期刊论文

  • 发表日期：

    2025年7月9日

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  • Jihe Chen,
  • Xi He,
  • Yimin Ning,
  • Tommy Tanu Wijaya,
  • Jinhai Liu

摘要

Artificial Intelligence (AI) has sparked a revolution in education, but understanding the factors influencing STEM teachers’ intentions to use AI for educational innovation remains a challenge. This study integrates the Task–Technology Fit (TTF) model and the Technology Acceptance Model (TAM) to explore how AI technologies influence STEM teachers’ willingness to engage in innovative teaching practices. Using a combination of Partial Least Squares Structural Equation Modeling (PLE-SEM) and Fuzzy-Set Qualitative Comparative Analysis (fsQCA), empirical analysis is conducted to examine the influence pathways and antecedent configurations. The study finds that Task–Technology Fit and Attitude toward Using Teaching Innovation are key factors influencing the willingness to innovate in teaching. While the effect of Perceived Ease of Use is relatively small, it still serves as a necessary precondition for optimizing teachers’ technological experience. The fsQCA reveals three pathways that promote STEM teachers’ use of AI for teaching innovation: technology–task fit-driven, technology attraction-driven, and hybrid-driven. The results indicate that adequate technical support systems and the adaptability of AI tools to teaching innovation requirements constitute essential prerequisites for promoting AI integration in STEM education. This study provides a theoretical foundation and methodological frameworks to guide AI integration in education, addressing the practical demands of educational administrators and technology developers in crafting effective AI implementation strategies for the sector.