Pre-service teachers' inclination to integrate AI into STEM education: Analysis of influencing factors

In the ever-evolving AI-driven education, integrating AI technologies into teaching practices has become increasingly imperative for aspiring STEM educators. Yet, there remains a dearth of studies exploring pre-service STEM teachers' readiness to incorporate AI into their teaching practices. This study examined the factors influencing teachers' willingness to integrate AI (WIAI), especially from the perspective of pre-service STEM teachers' attitudes towards the application of AI in teaching. In the study, a comprehensive survey was conducted among 239 pre-service STEM teachers, examining the influences and interconnectedness of Technological Pedagogical Content Knowledge (TPACK), Perceived Usefulness (PU), Perceived Ease of Use (PE), and Self-Efficacy (SE) on WIAI. Structural Equation Modeling (SEM) was employed for data analysis. The findings illuminated direct influences of TPACK, PU, PE, and SE on WIAI. TPACK was found to directly affect PE, PU, and SE, while PE and PU also directly influenced SE. Further analysis revealed significant mediating roles of PE, PU, and SE in the relationship between TPACK and WIAI, highlighting the presence of a chain mediation effect. In light of these insights, the study offers several recommendations on promoting pre-service STEM teachers' willingness to integrate AI into their teaching practices.

Practitioner notes

What is already known about this topic?

• The potential of AI technologies to enrich learning experiences and improve outcomes in STEM education has been recognized.
• Pre-service teachers' willingness to integrate AI into teaching practice is crucial for shaping the future learning environment.
• The TAM and TPACK frameworks are used to analyse teacher factors in technology-supported learning environments.
• Few studies have been conducted for examining factors of pre-service teachers' willingness to integrate AI into teaching practices in the context of STEM education.

What this paper adds?

• A survey was designed and developed for exploring pre-service STEM teachers' WIAI and its relationships with factors including TPACK, PE, PU, and SE.
• TPACK, SE, PU, and PE have direct impact on pre-service STEM teachers' WIAI.
• SE, PU, and PE have been identified as mediating variables in the relationship between TPACK and WIAI.
• Two sequential mediation effects, TPACK → PE → SE → WIAI and TPACK → PU → SE → WIAI, among pre-service STEM teachers were further identified.

Implications of this study for practice and/or policy

• Pre-service STEM teachers are encouraged to explore and utilize AI technology to enhance their confidence and self-efficacy in integrating AI into teaching practices.
• Showcasing successful cases and practical experiences is essential for fostering awareness of AI integration in STEM education.
• It is recommended to introduce AI education courses in teacher training programs.
• Offering internship and practicum opportunities related to AI technologies can enhance their practical skills in integrating AI into education.