What drives preservice teachers’ use of generative AI as instructional media? A structural and configurational analysis

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Introduction: The accelerating diffusion of Generative AI (GenAI) in education has sparked interest in understanding how preservice teachers adopt it as instructional media. Drawing on the "Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)" as a guided framework, this study examined cognitive, motivational, and contextual drivers of Generative AI use among preservice teachers in Ghana. Methods: The descriptive cross-sectional survey design was used and data were collected from 783 preservice teachers using validated questionnaires. The study used the "Partial Least Squares Structural Equation Modelling (PLS-SEM) and Fuzzy Set Qualitative Comparative Analysis (fsQCA)" to analyse the data. Results and discussion: The structural model revealed that behavioural intention strongly predicted GenAI use. Performance expectancy, perceived learning opportunity, perceived trust, and social impact significantly influenced intended behaviour. However, the conditions for facilitation, perceived learning opportunity, and perceived trust directly predicted actual use. Together, the model was able to explain 64.7% of the variance in behavioural intention and 68.8% in GenAI use, both using strong predictive relevance. FsQCA results revealed multiple sufficient configurations leading to high behavioural intention and GenAI use. This emphasises that diverse combinations of cognitive, institutional, and affective factors can drive adoption. The findings further highlight that perceived trust and learning opportunities are central to preservice teachers' engagement with GenAI. Also, behavioural intention and structural support remain necessary for sustained use. The study offers both theoretical and practical contributions for embedding GenAI literacy and innovative teaching techniques into teacher education programmes.

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