Ontological, Epistemological, and Axiological Foundations for AI based Learning Models: An Integrative Literature Review - Emita, Cicilia; Falah, Rivan Syahrul; Muslim, Suyitno; Djatmiko, Wisnu; Kandriasari, Annis (Ilomata International Journal of Social Science, 2026)
This paper examines the philosophical foundations of AI based learning models by integrating ontological, epistemological, and axiological perspectives into a unified conceptual framework. Although artificial intelligence has rapidly transformed educational environments, prior research has largely examined these philosophical dimensions in isolation, resulting in fragmented guidance for design and implementation. To address this gap, this study develops an integrative tripartite framework that explains how ontological structures, epistemological processes, and axiological principles jointly shape AI supported learning systems. Using an integrative literature review, this study analyses thirty two Scopus indexed journal articles published between 2015 and 2025, complemented by foundational philosophical works. Thematic synthesis identifies three interdependent components of AI based learning: the ontological dimension structures learners, data, algorithms, and educational contexts; the epistemological dimension explains how knowledge is co constructed, validated, and negotiated between humans and intelligent systems; and the axiological dimension articulates the values governing AI use, including human agency, fairness, accountability, and ethical responsibility. The main contribution of this study is a coherent conceptual framework with clearly defined components and application pathways for guiding the design, evaluation, and governance of AI based learning models. The novelty lies in explicitly integrating ontology, epistemology, and axiology into a single model, moving beyond prior fragmented approaches. The findings position AI integration as a multidimensional educational challenge rather than a purely technical endeavour and provide a structured foundation for developing AI supported learning systems that are pedagogically meaningful, ethically grounded, and socially responsible.