Japan’s nationwide Informatics curriculum and new generative‑AI guidelines create both opportunity and uncertainty for classroom use. We model teacher adoption in Japanese high‑school Informatics as a two‑stage process: expectation (perceived usefulness) → intention, and self‑use frequency → actual adoption, aligned with TAM/UTAUT. We conducted a Japan‑wide volunteer survey of Informatics teachers (n=104). Scales (5‑point Likert) measured expectation, concern, and intention; self‑use was coded 1 (“trial only”) to 4 (“almost daily”); adoption indicated whether teachers had already used AI in class. Multiple regression predicted intention; logistic regression predicted adoption. Intention was predicted by expectation (β=0.524, p<.001) and self‑use (β=0.267, p=.003), R²=0.500. Adoption was predicted only by self‑use (OR=2.329, 95% CI [1.219, 4.447], p=.010). Holding expectation and concern at their medians, predicted adoption rose monotonically with self‑use: 0.325 → 0.528 → 0.723 → 0.859; the classifier’s AUC was 0.679. The model connects policy to classroom by converting common reservations into manageable requirements: disclosure of AI use, source attribution, and verification logs of fact‑checking and revisions. For teacher professional development, we propose a short, repetitive practice loop (~15 minutes weekly for four weeks) to raise self‑use before whole‑class adoption. The practical message is simple and actionable: expectation drives intention; self‑use drives adoption. We contribute discipline‑specific numerical evidence and reusable instructional patterns and evaluation checklists that other systems can adapt.

Authors:
Shunsuke Inagaki, University of Yamanashi, Japan

About the Presenter(s)
Shunsuke Inagaki, Ph.D., is an Associate Professor at the University of Yamanashi. After 20 years teaching high school, he researches programming, data science, and problem-solving education, aiming to improve information education in schools.

Connect on Linkedin
https://www.linkedin.com/in/inagaki-shunsuke/

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