Machine learning integrated virtual and augmented reality in education: A systematic literature review and bibliometric analysis (2015–2025) - Denik Agustito; Esti Harini; Trisniawati Trisniawati; Eka Yulia Sari; Mohamad Basri Nadzeri; Krida Singgih Kuncoro; Betty Kusumaningrum; Dhimas Nur Setyawan (Universitas Sarjanawiyata Tamansiswa, 2025)
The integration of machine learning (ML) with immersive technologies—Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and Extended Reality (XR)—has generated significant momentum in educational innovation over the past decade. ML-enhanced immersive environments now support adaptive feedback, multimodal sensing, pose estimation, automated performance evaluation, and real-time learning analytics, offering new pathways for personalized and experiential learning. Despite rapid growth, existing research remains fragmented across domains such as engineering, health, arts, and language education, with limited synthesis explaining how these studies connect, evolve, and shape the knowledge structure of ML–VR/AR research. To address this gap, this study conducted a combined Systematic Literature Review (SLR) and bibliometric analysis of Scopus-indexed publications from 2015 to 2025. Eligibility assessment resulted in 58 studies included in the final analysis. Bibliometric findings show consistent growth in ML–VR/AR educational research, with publications spread across diverse journals and domains. This review offers a comprehensive mapping of scientific influence, thematic structure, and developmental trajectories in ML–VR/AR educational research. Findings provide a foundation for advancing adaptive immersive learning models, strengthening theoretical integration, and guiding future ML-driven XR innovations across educational contexts.