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The rapid integration of artificial intelligence (AI) into materials science has enabled unprecedented predictive capabilities across a wide range of properties and structures. However, the predominantly black-box nature of these models limits their epistemic role, confining them largely to correlative tools rather than instruments capable of supporting genuine scientific reasoning. This conceptual manuscript introduces a novel theoretical framework that delineates a structured pathway for validating AI systems as materials reasoning tools. Drawing on recent advances in explainable and interpretable AI, as well as philosophical accounts of scientific reasoning, the framework articulates a progressive sequence of validation stages: establishing transparency and interpretability, extracting mechanistically meaningful explanations, assessing reasoning fidelity through inferential behavior, and integrating AI systems as instruments within the broader scientific knowledge cycle. The approach is deliberately architecture-agnostic and avoids empirical prescriptions, focusing instead on the conceptual and epistemic conditions required for scientific legitimacy. By explicitly bridging predictive performance with explanatory depth, inferential robustness, and alignment with physical theory, the proposed pathway reframes how success in materials AI is evaluated. It provides a foundation for distinguishing advanced predictive engines from systems capable of contributing to hypothesis generation, theory refinement, and cumulative understanding. In doing so, the framework addresses persistent barriers to the acceptance of AI as a scientific partner in materials research. It offers a principled basis for future methodological and evaluative developments.