Publications and Research
Document Type
Article
Publication Date
Spring 5-29-2026
Abstract
Abstract
Higher education faces a convergence of pressures that no single institutional response has yet resolved: widening demographic diversity, pandemic-exposed structural fragility, and the persistent inequity of whose learning gets supported and how. Artificial intelligence (AI) has entered this landscape not as an optional enhancement but as an infrastructural reality— embedded in adaptive platforms, feedback systems, and early-alert protocols at institutions worldwide. Yet adoption has consistently outpaced governance, and tools celebrated for democratizing access carry real risks of encoding and amplifying the inequities they promise to address. This article introduces the AI-Augmented Pedagogy (AAP) framework, developed through a Sequential Explanatory Mixed-Methods Design grounded in a systematic synthesis of 312 peer-reviewed empirical studies, 68 institutional policy documents, and 26 grey literature sources. The framework is theoretically principled and empirically informed; its direct causal claims require prospective validation. Seven pillars structure the framework: Cognitive Symbiosis, Dynamic Cognitive Modulation, Interpretable Intelligence, Transdisciplinary Synthesis, Metacognitive Scaffolding, Embedded Moral Cognition, and Generative Knowledge with Critical Co-Creation. Synthesized studies report AI-mediated adaptive feedback effects of d= 0.40–0.76, average retention gain across modalities of 15 percentage points, and an approximately 38% reduction in routine instructional time. Original contributions include a Developmental Progression Model, a full-spectrum transparency mandate, a HECVAT-aligned tool-vetting protocol, and a proposed Epistemic Fluency Index to support cross-study comparison.
