Toward a Systems-Level Theory of AI-Experiential Learning Orchestration (AIELO)

Purpose This study advances the first systems-level AI-Experiential Learning Orchestration (AIELO) theory, systematically linking Kolb’s four-stage experiential cycle to concrete artificial-intelligence affordances while embedding an ethics-of-care rubric. It addresses the absence of an integrative, evidence-anchored, and ethically grounded framework guiding AI-enhanced experiential learning. Methodology An interdisciplinary mixed-methods design combined (i) a PRISMA-aligned random-effects meta-analysis of 46 AI-mediated experiential trials (N ≈ 4,500), (ii) sequence-clustering of ≈ 32,000 learner trajectories from open datasets, and (iii) bibliometric network analysis of 237 Scopus/Web of Science publications (2020-2025). Findings The meta-analysis revealed a moderate overall effect of AI on learning outcomes (Hedges’ g ≈ 0.50), with collaborative and immersive (VR) implementations outperforming AI tutoring. Learner-analytics identified “distributed” versus “massed” engagement patterns; consistent, spaced interaction predicted higher achievement. Bibliometric mapping uncovered six thematic clusters—AI literacy, experiential pedagogy, ethics, digital competence, adaptive analytics, and immersive media—signalling a converging multidisciplinary research front. Practical Implications AIELO positions AI as a co-orchestrator that (1) creates authentic concrete experiences through simulations and VR, (2) scaffolds reflective observation via analytics-driven feedback, (3) supports abstract conceptualization with adaptive content and intelligent tutoring, and (4) enables active experimentation through sandboxed practice environments, all governed by attentiveness, responsibility, competence, and responsiveness (Tronto, 1993). Originality/Value By unifying empirical evidence with experiential-learning and care ethics, AIELO offers scholars, educators, and policymakers a validated blueprint for designing responsible, high-impact AI learning ecosystems. It moves discourse beyond tool-centric or techno-utopian narratives toward a holistic, ethically balanced model that can adapt to emergent AI capabilities while safeguarding human-centered values.