Vocational Teachers’ Adoption of and Resistance to AI Teaching Tools: A Dual-Path Framework

As artificial intelligence becomes increasingly embedded in educational practice, how teachers evaluate and respond to AI teaching tools has become an important issue for understanding human–AI interaction and technology application in educational contexts. However, existing research has yet to provide a clear explanation of why teachers form divergent decisions—namely, adoption and resistance—toward the same technology. To address this issue, this study integrates the Artificial Intelligence Device Use Acceptance framework, Behavioral Reasoning Theory, and Diffusion of Innovation Theory to develop a dual-path analytical framework, aiming to uncover the evaluation and decision-making mechanisms underlying vocational teachers’ responses to AI teaching tools. Based on survey data collected from 503 Chinese vocational college teachers and analyzed using structural equation modeling, the results indicate that perceived attractiveness significantly enhances adoption intention through positive emotions, whereas risk barriers increase alternative attractiveness and thereby indirectly strengthen resistance intention. Usage barriers show no significant effect. In addition, trust significantly moderates the relationship between positive emotions and resistance intention. The findings suggest that teachers’ responses to AI teaching tools are not driven by a single behavioral tendency but are instead shaped by a dual-path decision mechanism in which cognitive evaluations and affective processing jointly operate, with a core trade-off between attractiveness and alternative attractiveness. This study deepens the understanding of behavioral divergence in AI teaching tool use by highlighting the role of evaluation and decision-making mechanisms in human–AI interaction contexts.