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The integration of artificial intelligence into materials science has accelerated discovery processes, yet the persistent challenge of data scarcity undermines the full potential of these technologies. This conceptual paper develops a novel theoretical framework for understanding small-data regimes in materials AI, emphasizing the interpretive dynamics that emerge when limited datasets intersect with domain knowledge and computational strategies. By synthesizing recent literature, the framework explains how scarcity influences model behavior through mechanisms of uncertainty amplification and knowledge integration, revealing interaction patterns between sparse empirical inputs and physics-informed priors. Analytical implications include enhanced epistemic reasoning about model reliability in low-data contexts, where trade-offs between generalization and specificity manifest in feedback structures that guide iterative refinement. Conceptual interpretations highlight steering logics that balance data-driven insights with theoretical constraints, fostering systems-level insights into how small-data environments reshape AI workflows in materials design. The framework underscores ethical considerations in deploying such systems, particularly regarding bias propagation under scarcity. Through a detailed textual description of a schematic figure, the paper illustrates these dynamics and offers integrative perspectives for advancing materials informatics without relying on large-scale data collection. Ultimately, this theory reorients focus toward resilient AI architectures that thrive amid informational constraints, promoting sustainable innovation in the field.