Toward an Interpretive Synthesis of Personalization, Co-Design, and Ethical Mediation in AI-Supported Higher Education: An Integrative Review of Agentic, Generative, and Adaptive Tutoring Systems

Generative artificial intelligence (AI) and adaptive learning technologies are expanding in higher education, raising questions about their alignment with pedagogical goals. This qualitative-integrative review synthesizes 57 publications (21 empirical, 25 conceptual or framework, 9 systematic reviews or meta-analyses retained for triangulation, and 2 design/development papers) from 2024 to 2026, employing Whittemore and Knafl’s methodology to examine AI-supported tutoring, implementation strategies, and learner/instructor experiences. Three interpretive themes organize this heterogeneous evidence: (1) personalization as a recurring target, from micro-level adaptation to curriculum design; (2) technology-pedagogy co-design as a recurring implementation pattern; and (3) contextual and ethical conditions surrounding adoption, including culture, policy gaps, well-being, and academic integrity. These themes are interpretive, not a validated framework, and draw on evidence of uneven strength. The present review does not estimate effect sizes or pool outcomes across studies; g = 0.70, reported by X. Wang et al. in a separate meta-analysis of 45 studies (2010–2022) of pre-generative adaptive systems, which is cited only as background, not as a finding of this review. Implications are framed as evidence-tiered hypotheses, while the layered model is offered as an interpretive scaffold for future research, not a confirmatory model.