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This review systematically examines conceptual models of the AI-materials scientist interface, tracing the evolution from AI as a passive computational tool to AI as an active collaborator capable of shared reasoning and autonomous contribution in materials discovery workflows. Drawing exclusively on 35 peer-reviewed publications spanning 2017–2025, the analysis integrates literature from human-computer interaction, artificial intelligence, and materials science to map the dominant metaphors, emerging conceptual shifts, existing interface models, and critical dimensions that define effective human-AI partnership. The tool metaphor, which positions AI strictly as a calculator, database, or predictor under full human control, is shown to dominate current practice yet reveals significant limitations once AI systems exhibit greater autonomy, opacity, and generative capacity. Conceptual shifts—moving from passive execution to active proposal, controlled operation to adaptive autonomy, and subordinate assistance to epistemic partnership—are documented as necessary preconditions for reframing AI as a scientific teammate. Existing models of the interface, including human-in-the-loop, human-on-the-loop, human-in-command, shared cognitive partnership, and full autonomy variants, are surveyed with concrete examples from materials research. In contrast, six core dimensions (autonomy level, communication modality, shared understanding, trust dynamics, goal alignment, and role flexibility) are articulated as the foundational axes along which collaboration quality can be assessed. Persistent gaps, such as the scarcity of empirical studies on real-world collaboration effectiveness and the absence of validated metrics beyond task performance, are identified, leading to targeted future directions that emphasize empirical teaming studies, adaptive interface design, and ethical frameworks for AI-scientist relationships. Ultimately, the review argues that materials science stands at a pivotal transition point where embracing AI as a collaborator, rather than a tool, will be essential for unlocking the next generation of accelerated, creative, and trustworthy discovery processes.