Beyond Detection: An Epistemic Limit of Purely Computational Misinformation Defense

The dominant paradigm for countering misinformation prioritizes computational detection, classification, and automated fact-checking. This paper argues that such approaches rest on an unexamined epistemic assumption that human critical capacity remains stable and sufficient to interpret, verify, and govern algorithmic outputs. We identify a structural blind spot in this paradigm, particularly salient in educational settings, where increasing reliance on generative artificial intelligence risks eroding the very epistemic capacities required for effective human–AI collaboration. To make this claim precise, we develop an epistemic modal logic framework that demonstrates the insufficiency of purely computational defenses to guarantee critical understanding in human agents. We conclude that technical defenses are necessarily contingent on prior educational investment in human epistemic virtues, reframing misinformation defense as a problem of socio-cognitive-technical resilience rather than algorithmic optimization alone.