Insights into Artificial Intelligence and Ethnoscience in STEM: Systematic Literature Review (SLR) for Pre-Service Physics Teachers

Yaspin Yolanda (PGRI Silampari University)
Maison (Universitas Jambi)
Damris. M (Universitas Jambi)
Bambang Heriyadi (Universitas Jambi)
Imam Arif Pribadi (Tomsk Polytechnic University)

Journal of Innovation in Applied Natural Science
Vol. 2 No. 2 (2026): Journal of Innovation in Applied Natural Science

Background: Integrating Artificial Intelligence (AI), ethnoscience, and STEM education is increasingly important for preparing pre-service physics teachers to address technological, interdisciplinary, and culturally responsive demands. However, research in Indonesia remains fragmented, with AI, STEM, and ethnoscience investigated as separate domains, limiting integrated approaches to physics teacher preparation. Aims: This study identifies publication trends, dominant themes, methodological characteristics, and gaps in integrating AI, ethnoscience, and STEM within Indonesian pre-service physics teacher education. Methods: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, an integrative literature review examined publications from 2015 to 2026 from Google Scholar, IEEE Xplore, Scopus, and ERIC. Following title, abstract, and full-text screening, 51 articles were selected. Data were analyzed using descriptive bibliometrics, thematic analysis, and gap analysis. Results: Three trends emerged. First, research emphasizes AI- and STEM-enhanced learning, conceptual understanding, and 21st-century competencies. Second, ethnoscience studies promote local wisdom, contextual learning, cultural literacy, and instructional resources, but remain disconnected from AI-supported STEM learning. Third, comprehensive frameworks integrating AI, ethnoscience, and STEM for pre-service physics teacher education remain scarce. Developmental and qualitative approaches dominate existing studies, while mixed-methods, longitudinal, and intervention-based research remains limited. Gaps persist in examining pre-service teachers’ AI literacy, TPACK, prior knowledge, and capacity to design culturally contextualized STEM learning supported by AI. Conclusion: The findings reveal a research gap and provide a foundation for developing empirically validated, culturally responsive frameworks integrating AI, STEM, and ethnoscience to strengthen AI literacy, TPACK, STEM competencies, and contextual pedagogical practice in physics teacher education.