Teachers' Stages of Concern in Adopting Generative Artificial Intelligence for Science Education: A Systematic Review
DOI:
https://doi.org/10.54536/ajmri.v5i5.8578Keywords:
Adoption, AI Literacy, Artificial Intelligence, Chatgpt, Concerns-Based Adoption Model (Cbam), Educational Innovation, Generative Artificial Intelligence, Prisma, Science Education, Science Teachers, Systematic ReviewAbstract
This systematic review, in the context of the Concerns-Based Adoption Model (CBAM) as the theoretical framework, reviewed science teachers' concerns about the implementation of Generative Artificial Intelligence (GenAI) tools in science education. The study aimed to investigate the stages of concern science teachers show, analyze the factors that cause concerns and assess the strategies that aid in facilitating the adoption of GenAI in science teaching. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was used to structure the review, and a narrative thematic synthesis was applied to categorize and synthesize the results from the different studies. Literature was identified through Google Scholar by systematically developed search terms that included combinations of the concepts of GenAI tools and teacher concern constructs, the full Boolean search strategy is reported in the Methods section for the purposes of replicability. A total of 432 records were identified and then screened for eligibility and then peer-reviewed empirical studies published from 2022 to 2026 were included. The findings show that the majority of secondary science teachers are at the early CBAM stages, suggesting that the majority of teachers are still preoccupied with understanding and managing GenAI rather than understanding how it can influence student learning. It was found that concern level and intention to adopt work via different psychological mechanisms: adoptive intentions can be stronger than positive beliefs at high concern stages. This gap indicates the need for theory-based empirical studies on GenAI adoption in secondary science education. This review drew from a single database and did not include a formal quality appraisal, its conclusions should be read as an initial, theory-building synthesis rather than a fully instrument-validated account. These boundaries are made explicit in the methods sections.
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Copyright (c) 2026 Renald A. De Vera, Lilibeth G. Abrogena, Jayferson G. Panilo
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