The development and application of artificial intelligence (AI) has empowered and accelerated the process of education and teaching transformation. Although prior studies have examined the forms of integrating AI into education, insights into the effective factors impacting pre-service teachers’ AI assisted instruction intention (AI-AII) are rather limited. Considering this gap, this study constructed a structural model among AI-AII, AI pedagogical content knowledge (AI-PCK), AI technological knowledge (AI-TK), performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC). Data were collected from 1391 pre-service teachers in China. Results of the modeling effort indicate that the pre-service teachers’ AI-PCK, EE, PE, SI, and FC positively predict their AI-AII. However, pre-service teachers’ AI-TK had indirect effects on their AI-AII. These insights are important for educators and policymakers to consider in designing teacher education and professional development related to foster pre-service teachers’ behavioral intention to use AI in teaching.

The online questionnaire comprised of two sections. The initial section focused on gathering background information from participants, encompassing aspects such as gender, university category, grade level, majors, enrollment in educational technology courses, and familiarity with AI-assisted teaching. The second part sought to assess the intention of pre-service teachers to utilize artificial intelligence in their teaching. This component had seven constructs: Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, AI-TK, AI-PCK, and Behavioral Intention. Data analysis consisted of 4 stages: exploratory factor analysis (EFA), Confirmatory Factor Analysis (CFA), reliability analysis, and Structural Equation Modeling (SEM). Descriptive statistical analysis was conducted on all samples using SPSS 27.0. First, a randomly selected 50% of cases (N = 696) were used for performing the EFA of instrument in IBM SPSS 27.0 to clarify the factors. The sample size of EFA met the subject to item ratio of 10:1 suggested by Gorsuch（1983）. Then the structural equation modelling (SEM) technique was employed using AMOS 26.0 with the remaining 50% (N = 695) of observations to examine the measurement model and the structural model. Subsequently, latent variable path analysis was conducted in order to evaluate the hypotheses.

Our results showed that AI-TK exerted indirect effects on pre-service teachers’ behavioral intention to AI for assisted teaching. This indicated that AI may not have always been a preferred tool among teachers who were aware of how AI could enhance teaching and learning in general unless they understood the pedagogical benefits. Further, AI-based tools could emphasize their pedagogical advantages (such as timely and personalized feedback). The outcomes demonstrate that both AI-TK and AI-PCK possess direct predictive influence over performance expectancy and effort expectancy. In accordance with UTAUT theory, this study confirmed that PE, EE, SI and FC positively influenced pre-service teachers’ behavioral intention to use AI for assisted teaching. Among these factors, effort expectancy serves as a direct predictor of pre-service teachers' inclination to utilize AI. This underscores the necessity for governmental bodies or educational institutions aiming to foster the amalgamation of AI and teaching in universities to aid pre-service teachers in comprehending AI's utility for their future instructional practices.

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