Category Error and Epistemic Artifacts in AI-Assisted Symbolic Cognitive Systems

This paper examines the epistemic status of AI-assisted symbolic cognitive systems and argues that such systems are frequently subject to a persistent category error. Rather than being assessed as empirical theories of cognition, complete computational architectures, or psychological models, these systems are more appropriately understood as epistemic artifacts with an intermediate methodological function. Drawing on classical contributions from the philosophy of science and cognitive science—including research programmes, heuristic reasoning, and distributed cognition—the paper clarifies the type of epistemic work these systems perform. It proposes criteria for their proper evaluation, emphasizing internal coherence, organizational power, and conceptual clarity, while explicitly rejecting demands for direct empirical validation or computational performance as methodologically misplaced. By repositioning AI-assisted symbolic systems within the landscape of scientific inquiry, the paper aims to enable more accurate criticism, responsible development, and cumulative theoretical progress.