AI, METACOGNITION, AND DIGITAL LITERACY IN PHYSICS EDUCATION: SYSTEMATIC LITERATURE REVIEW AND BIBLIOMETRIC ANALYSIS

Purpose – This study critically examines the rapid expansion of artificial intelligence (AI) in digital learning, with a specific focus on how these technologies intersect with metacognitive skills and digital literacy in science/STEM education. This study explicitly investigates their conceptual integration and identifies a substantiated research gap in physics learning contexts. Methodology – A systematic literature review (SLR) combined with bibliometric analysis was conducted in accordance with the PRISMA 2020 protocol. Articles were retrieved from Scopus using structured TITLE-ABS-KEY queries and limited to peer-reviewed journal articles (2020–2025) within the Social Sciences domain. From 2,670 initial records, 47 articles met predefined inclusion criteria (n = 47). Bibliometric mapping was performed using VOSviewer, followed by thematic synthesis employing open, axial, and selective coding to analyze AI intervention types, pedagogical mechanisms, and measurement approaches for metacognition and digital/AI literacy. Findings – Bibliometric evidence indicates a sharp rise in publications from 2023 onward, with education and educational technology channels dominating the dissemination landscape. Metadata screening further revealed an explicit research void regarding the integrated study of AI, metacognition, and digital literacy in physics learning. Thematic synthesis suggests AI can enable more personalized learning trajectories, richer formative feedback, and improved self-regulation supports that align with metacognitive development and digital literacy practices. Contribution – This study provides a bibliometrically grounded knowledge map of AI–metacognition–digital literacy research and conceptual adaptation framework proposing physics-oriented AI learning design principles and task-based assessment directions to guide future empirical research.