ArticleFrontiers in psychology2026
The mechanisms influencing AI teaching readiness among preservice STEM teachers: an empirical analysis based on PLS-SEM and fsQCA.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: With the rapid integration of artificial intelligence (AI) technologies into basic education, preservice teachers' AI teaching readiness has become an important factor influencing future AI-supported teaching practices and instructional innovation. Drawing on technology readiness (TR), the technology acceptance model (TAM), and the stimulus-organism-response (SOR) framework, this study developed a mechanism model to explain AI teaching readiness among preservice STEM teachers. Methods: This study empirically examined the proposed model using partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). In the model, school support was conceptualized as an external stimulus; technology readiness traits, including optimism, innovativeness, discomfort, and insecurity, together with technology-related cognitive appraisals, including perceived usefulness and perceived ease of use, were conceptualized as internal organismic states; and AI teaching readiness was conceptualized as the response. Results: The findings showed that perceived usefulness and perceived ease of use were key factors driving preservice STEM teachers' AI teaching readiness. School support influenced AI teaching readiness through multiple chain-mediating paths involving technology readiness traits and technology-related cognitive appraisals. The PLS-SEM results indicated that the effects of school support on innovativeness and insecurity were not significant. Further, the fsQCA results revealed that, in some high-readiness configurations, school support worked jointly with innovativeness, insecurity, and technology-related cognitive appraisals, while high innovativeness could also compensate, to some extent, for insufficient school support. Discussion: The cross-analysis of PLS-SEM and fsQCA revealed both linear pathways and configurational effects in the formation of AI teaching readiness, suggesting that it is not driven by a single factor but by combinations of multiple conditions. This study enriches the application of TR, TAM, and SOR in the context of AI educational technology adoption and provides practical implications for teacher education institutions and school administrators seeking to enhance preservice teachers' AI teaching readiness.
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