Artificial intelligence has rapidly become a significant educational technology with the potential to transform teaching and learning processes. In science education, AI and GenAI tools may support inquiry practices, modeling, scaffolding and adaptive feedback and facilitate access to diverse representations of complex concepts (Crompton & Burke, 2023; Jia et al., 2024). However, these benefits depend on teachers’ capacity to integrate AI meaningfully and responsibly, considering pedagogical fit, scientific accuracy and ethical implications.

Research increasingly reports that teachers and preservice teachers demonstrate curiosity toward AI, however they often feel inadequately prepared to use it effectively (Yue et al., 2024). Although AI-TPACK has been introduced as a framework to conceptualize teacher knowledge required for AI integration (Celik, 2023; Ning et al., 2024; Yao, 2021), only measuring PSTs’ AI-TPACK is not sufficient for supporting their professional development. It is also essential to examine factors that may shape the development of AI-TPACK.

PSTs’ technology adoption perceptions and their behavioral intention (BI) to use AI can play a critical role in such readiness. The Unified Theory of Acceptance and Use of Technology (UTAUT) model (Venkatesh et al., 2003) provides a well-established framework for explaining why individuals accept and use emerging technologies. UTAUT proposes that performance expectancy (PE), effort expectancy (EE), social influence (SI) and facilitating conditions (FC) influence behavioral intention and usage behavior of new technologies. Although the model was developed in workplace settings, it has been widely adapted and applied in educational research contexts, including AI adoption (An et al., 2023; Chen et al., 2025; Xue et al., 2024).

Recent AIEd studies show mixed findings regarding how AI-TPACK relates to UTAUT constructs and BI. Some studies report positive associations suggesting that greater competence supports stronger technology acceptance (An et al., 2023), while others show weak, non-significant or even negative relationships (Parviz & Arthur, 2025; Runge et al., 2025). These mixed results call for further research, particularly among preservice teachers, who are still developing professional competencies and have limited classroom experience. For PSTs, intention may be a stronger indicator of future adoption behavior and a motivational force that encourages further engagement in professional development, which may in turn strengthen competence.

Therefore, this study examines UTAUT perceptions and behavioral intention as predictors of preservice science teachers’ AI-TPACK by addressing the following research question: To what extent do preservice science teachers’ UTAUT perceptions and behavioral intention to use AI predict their AI-TPACK in science education?

This study employs a quantitative correlational design. The participants were 544 preservice science teachers enrolled in second, third or fourth year of science education programs. First-year PSTs were excluded due to limited coursework exposure relevant to the constructs. The sample included 444 females (81.6%) and 100 males (18.4%), with 159 second-year (29.2%), 215 third-year (39.5%) and 170 fourth-year (31.3%) participants. AI-TPACK was measured using an adapted AI-TPACK Scale developed for preservice science teachers (93 items, eight factors), adapted from MaKinster et al. (2010), Celik (2023) and An et al. (2023). Items were rated on a 5-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (5), with higher scores indicating greater perceived competence. Component scores were calculated as the means of their respective items. Behavioral intention and technology acceptance perceptions were measured using a UTAUT-based questionnaire adapted from An et al. (2023) for AI usage in educational contexts. The instrument included five constructs: performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), social influence (SI) and behavioral intention (BI). Items were translated into Turkish and reviewed by bilingual experts to ensure linguistic equivalence. In terms of the factor structure, the adapted scale included the five constructs found in the original scale (PE, EE, FC, SI and BI) (An et al., 2023). To address the research question, correlation analyses and multiple regression analyses were conducted to examine the extent to which UTAUT constructs and behavioral intention explain the variance in AI-TPACK.

Correlation analyses indicated that AI-TPACK was significantly and positively correlated with all UTAUT constructs, though strength varied. AI-TPACK was moderately to strongly associated with EE (r = .509, p < .001), PE (r = .418, p < .001) and BI (r = .393, p < .001). Moderate correlation was found with FC (r = .361, p < .001), while SI showed a small but significant correlation (r = .137, p < .001). Multiple regression results indicated that four predictors significantly explained variance in AI-TPACK: EE, FC, PE and BI. The model was statistically significant (Adjusted R² = .391, F(4, 539) = 88.221, p < .001), explaining 39.1% of variance in AI-TPACK. EE was the strongest predictor (β = .360) accounting for 10.5% of the unique variance in AI-TPACK, followed by FC (β = .222), PE (β = .186) and BI (β = .139). SI was not retained in the final model. Findings suggest that perceptions of ease of use (EE) and facilitating conditions (FC) represent key factors for strengthening preservice science teachers’ readiness to integrate AI. In addition, perceived usefulness (PE) and behavioral intention to use AI (BI) contribute uniquely, though to a smaller extent. These results indicate that both motivational perceptions and enabling conditions are associated with PSTs’ AI-TPACK and may inform targeted interventions in teacher education programs, such as practice-based AI integration tasks, infrastructure support, and pedagogically grounded AI training. Notably, prior AIEd studies frequently position AI-TPACK as a predictor of behavioral intention and effort expectancy, whereas the present findings indicate that UTAUT-related perceptions and intention also significantly predict AI-TPACK. This pattern may suggest a mutually reinforcing association between competence and intention, which should be examined through longitudinal or SEM-based designs.

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