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Path analysis demonstrates that AI training and pedagogical knowledge enhance AI acceptance in pre-service teachers, highlighting the need for targeted coursework.
Based on the technology acceptance model (TAM), pre-service teachers’ acceptance of artificial intelligence (AI) is crucial in predicting their intentions to use AI in future teaching, as well as for their actual usage of AI. However, current research offers limited insights into the role of factors regarding usage intentions and behaviors. In particular, AI-related teacher training courses and AI-related technological pedagogical content knowledge (AI-TPACK) might be relevant, but are empirically underinvestigated within the TAM. This study addresses these gaps by investigating the relationships between pre-service teachers’ participation in AI-related courses, their self-reported AI-TPACK, their perceptions of AI’s usefulness and ease of use, and both their intention and actual usage of AI. Using path models with data from 143 pre-service teachers, the results revealed that participation in AI-related courses related positively to AI-TPACK and perceived AI-related usefulness. Further, AI-TPACK was positively related to perceived AI-related usefulness and ease of use, which in turn positively related to the behavioral intention to use AI in future teaching and the actual usage of AI for profession-related tasks in teacher training. The study results extend the existing research on TAM and highlight the consideration of participation in AI-related courses and AI-TPACK as further factors in understanding pre-service teachers’ AI acceptance.
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Runge et al. (2025) studied this question.
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