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The integration of artificial intelligence (AI) into materials science has transformed the landscape of discovery and insight generation, enabling rapid analysis of complex datasets and simulation of material behaviors at unprecedented scales. However, the reproducibility of AI-generated insights remains a pivotal concern, as it underpins the epistemic validity of claims derived from such systems. This conceptual paper develops a novel theoretical framework that interprets reproducibility not as a static attribute but as an emergent property arising from dynamic interactions among data ecosystems, algorithmic architectures, and human interpretive practices. By synthesizing literature on AI trustworthiness and materials informatics, the framework elucidates the systemic conditions—such as data lineage transparency, algorithmic feedback loops, and ethical epistemic alignments—that must align for AI-derived claims to sustain scrutiny across contexts. It emphasizes interaction dynamics where data quality influences model robustness, while human oversight modulates algorithmic outputs, fostering a balanced ecosystem for reliable insights. Ethical reasoning is integrated throughout, highlighting trade-offs between computational efficiency and interpretive depth. This approach shifts focus from isolated reproducibility metrics to holistic systems-level insights, offering guidance for scholars and practitioners in applied AI for materials science. Ultimately, the framework advocates for a steering logic that prioritizes integrative processes over predictive assertions, ensuring that AI contributions enhance rather than undermine the foundational integrity of materials knowledge.