Mapping research trends in artificial intelligence for K–12 teaching and learning: a scientometric analysis

Abstract

This study examines the evolution of artificial intelligence (AI) research in K–12 teaching and learning by identifying major research trends, thematic structures, and emerging scholarly directions. A scientometric analysis was conducted using a validated corpus of 1,154 Scopus-indexed documents published between 2016 and May 2026. To strengthen corpus reliability, the dataset underwent content-level validation through abstract screening, duplicate detection, and thematic relevance assessment focused on K–12 educational contexts. Co-citation analysis, clustering algorithms, and citation burst detection were performed using CiteSpace to identify influential publications, thematic clusters, and temporal patterns of scholarly influence. The findings reveal rapid growth in academic production after 2020, particularly following the emergence of generative AI technologies. Four major research clusters were identified: (1) artificial intelligence literacy, focused on competency-based AI education and ethical awareness; (2) large language models (LLMs), centred on generative AI, conversational systems, and AI-mediated assessment; (3) early childhood education, emphasising developmentally appropriate AI learning experiences and playful pedagogies; and (4) K–12 context, addressing broader educational, ethical, and societal implications of AI integration in schools. Citation burst analysis revealed a progression from foundational AI literacy frameworks and constructionist machine learning pedagogies toward curriculum co-design, teacher readiness, and human-centred AI integration. Overall, the findings indicate that AI research in K–12 education is evolving toward more pedagogically grounded, ethically informed, and student-centred educational models while simultaneously highlighting challenges related to teacher readiness, curriculum adaptation, and equitable AI integration in school contexts.