Exploring the Influence of Artificial Intelligence Adaptation on Pre-Service Science Teachers’ Pedagogical Content Knowledge

Abstract

Background and Objectives: In the 21st century learning environment, the integration of emerging technologies has transformed the educational landscape. For pre-service science teachers, the challenge lies in developing strong pedagogical content knowledge (PCK) while also adapting to rapidly evolving technologies. This study aimed to explore how Artificial Intelligence (AI) tools influence the development of PCK among pre-service science teachers in the fields of physical and biological sciences. Specifically, it investigated their lived experiences of adapting to AI in lesson planning, instruction, and assessment, contributing to the broader discourse on AI integration in teacher education. Methodology: This qualitative study employed a descriptive phenomenological approach to capture the authentic experiences of six (n = 6) fourth-year pre-service science teachers specializing in physical and biological sciences at a state university in Bataan, Philippines. Participants were selected via purposive sampling based on their documented experience with AI tools in coursework and teaching demonstrations. Data were analyzed using descriptive phenomenological procedures, including bracketing, horizontalization, clustering, and textualization to identify the essence of participants’ experiences.  Results: The findings revealed that AI has a significant influence on all five domains of PCK. In teaching orientation, pre-service teachers embraced learner-centered strategies but raised concerns about AI’s pedagogical implications. For curricular knowledge, AI was useful in aligning instruction with standards, though participants emphasized its limitations in content depth. Instructional strategies were enhanced through AI-powered simulations and visuals, promoting interactivity. In the assessment, AI improved accuracy and efficiency, yet participants stressed the importance of teachers’ professional judgment. Lastly, regarding understanding learners, AI was seen as supportive, but concerns remained about its ability to address emotional and contextual learning factors. Discussions: AI is instrumental in supporting learner-centered approaches, promoting differentiated instruction, and aligning lesson plans with curricular standards. These themes indicate that AI can enhance teacher preparedness by offering practical solutions for instructional planning and responding to the diverse needs of learners. Pre-service teachers leverage AI to create meaningful learning experiences and more efficient assessments, yet they also express valid concerns about overreliance, equity, and the need for human judgment. The themes collectively underscore the dual role of AI as an enabler of innovation and as a potential source of pedagogical tension. These findings emphasize the importance of integrating training in AI use within teacher education to inform policy at the classroom and institutional levels, promoting ethical and pedagogically sound practices. Conclusions: Pre-service science teachers view AI as a helpful tool for lesson planning, assessment, and tailoring instruction to learner needs. AI supported teachers in generating materials, aligning lessons with curricular standards, and providing differentiated strategies. However, challenges such as overreliance on AI, risks to learners’ critical thinking, and the need for teacher oversight were also identified. While AI can enhance pedagogical content knowledge (PCK) and classroom practice, its effectiveness depends on thoughtful integration and continued professional development. These findings have implications, especially for educators and policymakers in comparable teacher education contexts, emphasizing the need to bridge the gap between technological advancement and pedagogical effectiveness.