Artificial Intelligence in Science Teaching: An Inductive Typology of Teacher Use and Its Alignment with Artificial Intelligence Literacy Frameworks

Artificial intelligence (AI) is increasingly recognized as a transformative force in education; however, limited attention has been given to systematically examining how teachers represent AI use within subject-specific contexts. This study therefore aims to develop an empirically grounded classification of AI use in science education on the basis of an analysis of 795 Scopus-indexed journal articles. Using a qualitative document-based approach, the article abstracts were inductively analyzed to identify recurring pedagogical functions attributed to AI in science teaching. Six nonexclusive categories emerged: instructional delivery and pedagogy, assessment and feedback, lesson planning and curriculum design, interactive agents, simulations and virtual environments, and teacher professional development and support. In a second analytical stage, these categories were interpreted through the Organization for Economic Cooperation and Development (OECD) AI literacy framework to examine how documented practices align with broader domains of AI engagement. The findings indicate that AI is predominantly framed as a support resource for classroom interaction and assessment processes, with strong alignment with engagement-oriented and design-mediated modes of AI use. Overall, the literature positions teachers as pedagogical integrators who interpret and structure AI within existing instructional practices rather than as technical developers. The study therefore offers a large-scale, practice-oriented typology of AI use in science education and provides an analytically grounded bridge between empirical representations and AI literacy discourse.