A Pedagogical Framework and Its First Classroom Implementation in Response to Automation Bias, Cognitive Debt, and the Verification Paradox

Generative AI (GenAI) has become cognitive infrastructure in higher education, yet creates a verification paradox: student reliance peaks where task complexity is highest, objective accuracy lowest, and perceived correctness remains inflated (46-point calibration gap). This paper presents the ACTIVE Framework, which is a six verification principles operationalized as a five-step workflow (Assess, Constrain, Inspect, Verify, Explain), and its first classroom implementation at Deggendorf Institute of Technology.Grounded in a three-wave longitudinal study (N=21, 36 and 23) documenting automation bias, plausibility pressure, and metacognitive miscalibration, the framework was delivered through a two-module lecture design across engineering disciplines. Key components include explicit verification protocols, human-in-the-loop documentation, confidence calibration training, and a gradable assessment architecture that renders verification teachable.The implementation demonstrates feasibility and acceptability in real course conditions while identifying common student failure modes (source-monitoring errors, fluency substitution). Unlike generalized AI literacy advocacy, ACTIVE offers a replicable instructional model with embedded assessment of the otherwise invisible verification process.