Artificial intelligence and cultural literacy in physics education for lifelong learning: a bibliometric analysis - Dafid Slamet Setiana Setiana; Betty Kusumaningrum; Sony Yunior Erlangga (Prodi Pendidikan Fisika Universitas Sarjanawiyata Tamansiswa, 2026)
Research on Artificial Intelligence (AI) and cultural literacy in physics education has gained increasing attention in recent years, reflecting the growing convergence of technological innovation and sociocultural perspectives in lifelong learning contexts. However, a comprehensive overview of publication trends, intellectual structures, and thematic development in this emerging domain remains limited. This study employed a bibliometric approach guided by the PRISMA protocol to systematically analyze research on AI and cultural literacy in physics education published between 2019 and 2026. A total of 24 peer-reviewed journal articles indexed in Scopus were identified, screened, and included. Bibliographic data were analyzed using the Bibliometrix R package to examine annual scientific production, author productivity, collaboration networks, and conceptual structures through keyword co-occurrence, thematic mapping, and factorial analysis. The findings indicate that research output was minimal in the early years but increased sharply after 2024, reflecting a recent surge of interest driven by advances in machine learning and generative AI. Author productivity follows a highly distributed pattern, with most contributors publishing only once and limited long-term research continuity. The intellectual structure is centralized, with AI acting as the dominant hub connecting key concepts such as students, learning, and higher education. Thematic analysis reveals a core–periphery structure, where foundational themes (e.g., AI and students) are highly relevant but underdeveloped, while learning and education function as well-developed motor themes. However, cultural literacy remains weakly integrated within the thematic network. The evolution of research in this field indicates a shift toward AI-driven, data-informed learning systems, yet highlights the need for stronger integration of cultural and pedagogical dimensions to support inclusive and meaningful physics education in lifelong learning environments.