When AI Looks Right, but Fails: Designing for Real Adoption in Higher Education

AI is rapidly reshaping higher education—but many implementations that appear successful at launch fail to deliver durable value. In one instructional setting, students used AI to generate process models that appeared complete and technically correct, yet were unable to explain tradeoffs or adapt their work. The outputs passed initial validation, but the underlying capability had not developed.This pattern extends beyond the classroom. Across institutional systems, AI can produce outputs that appear correct while masking gaps in validation, weakening human judgment, and exposing governance weaknesses under real-world conditions. This session introduces a practical model for system durability—grounded in validation, adoption, and governance—and shows how institutions can move beyond surface-level success toward sustained capability and resilience.