Generative AI (GenAI) has not broken higher education; it has exposed long-standing systemic fractures. This paper argues that traditional pedagogies, rooted in formalist epistemologies, are misaligned with the realities of a world mediated by thinking machines. Blending systems analysis with epistemological critique, it diagnoses higher education’s foundational incoherence and introduces two critical frameworks — epistemic laundering and epistemological vector space — to map and address this dissonance. Building on this foundation, the paper proposes Critical AI Literacy as a model that emphasizes epistemic awareness alongside technical fluency. It provides a structural analysis of GenAI’s limitations and outlines practical strategies including RAG tutors, cognitive partnerships, interactive content generation, and skill-gap training. Ultimately, this paper frames GenAI not as a disruptive threat, but as a catalyst for reimagining learning design, moving beyond performance-driven education toward authentic, adaptive expertise in a dynamic knowledge landscape.
Professor at George Washington University
I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.
Latest posts by Ryan Watkins (see all)
- Towards interactive evaluations for interaction harms in human-AI systems - April 29, 2025
- How Generative AI Fixes What Higher Education Broke - April 28, 2025
- AI for Accessible Education: Personalized Audio-Based Learning for Blind Students - April 25, 2025