Design of an Artificial Intelligence-based Personalized Learning Governance Framework - Ucu Nugraha; Sri Titi Handayani; Hernalom Sitorus; Agus Nursikuwagus; Yeffry Handoko Putra; Rio Yunanto (Universitas Islam Indragiri, 2026)
The rapid advancement of Artificial Intelligence (AI) in education has created significant opportunities for personalized learning while simultaneously introducing governance challenges for learners with disabilities. Existing studies have examined AI, adaptive learning, accessibility, and inclusive education; however, these areas remain fragmented and lack an integrated governance-oriented framework. This study aims to develop a Personalized Learning Governance Framework (PLGF) to support inclusive digital literacy through a systematic literature review and bibliometric analysis. The research methodology consisted of Scopus database retrieval, PRISMA-based screening, Biblioshiny-assisted bibliometric analysis, literature synthesis, gap identification, and conceptual framework development. From 196 Scopus-indexed records published between 2023 and 2026, a total of 97 studies were selected for analysis. The findings reveal strong conceptual relationships among AI, personalized learning, inclusive education, accessibility, disability, and ethical technology; however, their systematic integration remains limited. The proposed Personalized Learning Governance Framework (PLGF) comprises five interconnected layers: Learner Disability Profile Input, Machine Learning Personalization Engine, Inclusive Accessibility Adaptation, Governance and Ethical Control Center, and Digital Literacy Outcome Evaluation. The framework provides a comprehensive governance model for supporting accountable, inclusive, and AI-driven personalized learning systems while promoting equitable digital literacy for learners with disabilities.