This preprint introduces a structural interpretive framework for evaluating the influence of artificial intelligence on human learning processes. Contemporary debates frequently collapse fundamentally distinct cognitive categories—output quality, conceptual understanding, memory retention, operational competence, and adaptive reasoning—into a single undifferentiated notion of “learning.” This conceptual compression produces invalid measurements, unstable conclusions, and misleading educational policies. The framework distinguishes between output optimization (improved structure, navigation, productivity, and informational efficiency) and cognitive formation (internalization, abstraction, durable memory, and independent reasoning). It demonstrates that AI impact is domain‑dependent, varying significantly across fundamental sciences, engineering environments, information‑intensive professions, and operational systems. The work argues that AI functions not as a universal cognitive replacement, but as a dynamic interpretive layer that restructures symbolic labor, modifies entry pathways into professions, and shifts the balance between internal cognition and externalized intelligence. Without separating these categories, educational research risks producing exaggerated claims, invalid assessments, and ideologically driven narratives. This framework provides a methodological foundation for researchers, universities, regulators, and policy institutions seeking to evaluate AI’s role in education with conceptual precision rather than generalized assumptions.
Oleg Zmiievskyi (Fri,) studied this question.