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Shunsuke Inagaki, University of Yamanashi, JapanAbstract
Generative AI (GenAI) is rapidly entering schools, but teacher adoption is shaped not only by positive expectations (e.g., perceived usefulness) and concerns (e.g., accuracy, copyright, and governance) but also by teachers’ everyday experience using these tools in their own work. This study examines a two-stage model of GenAI adoption among Japanese high school Informatics teachers: (1) intention to use GenAI in class and (2) classroom adoption (whether GenAI has already been used in class). A nationwide online survey was administered in May 2025 to Informatics teachers across Japan (n = 104). Expectations, concerns, and intention were measured with multi-item scales; self-use frequency was measured on a four-level ordinal scale; and classroom adoption was measured as a binary outcome. Analytic sample sizes varied slightly by model due to item nonresponse. In Stage 1, ordinary least squares regression showed that expectations (B = 0.67, p < .001) and self-use frequency (B = 0.25, p = .003) predicted stronger intention, whereas concerns were not a significant predictor. In Stage 2, logistic regression showed that self-use frequency predicted classroom adoption (OR = 2.33, p = .010), while expectations and concerns were not significant when self-use was included. Predicted adoption probabilities rose from 0.33 (trial only) to 0.86 (almost daily self-use) at median expectations and concerns (AUC = 0.679). These findings suggest that expectations are sufficient to generate intention, but sustained self-use may be a practical bridge to classroom adoption. Implications are discussed for professional development that builds short, authentic self-use routines and for school-level guidelines that operationalise key concerns as simple, checkable classroom rules.
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Conference: IICE2026Stream: Design
This paper is part of the IICE2026 Conference Proceedings (View)
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To cite this article:
Inagaki S. (2026) Teacher Self-Use Predicts Classroom Adoption of Generative AI Among Japanese High School Informatics Teachers ISSN: 2189-1036 – The IAFOR International Conference on Education – Hawaii 2026 Official Conference Proceedings (pp. 183-197) https://doi.org/10.22492/issn.2189-1036.2026.18
To link to this article: https://doi.org/10.22492/issn.2189-1036.2026.18
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