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The ambiguous usage of the term “surprise” in AI-guided discovery literature represents a significant conceptual barrier in artificial intelligence for materials science. Surprise is variously treated as a statistical anomaly flagged by machine learning models, a human psychological state of unexpectedness that prompts belief revision, an information-theoretic measure of divergence between prior and posterior beliefs, or an unexpected breakthrough that leads to a genuine scientific advance. This lack of precision confuses researchers, fragments the literature, and impedes the systematic design of AI systems capable of deliberately cultivating the forms of unexpectedness that drive materials innovation. This paper proposes a precise typology of scientific surprise consisting of four distinct types—predictive surprise, representational surprise, discovery surprise, and methodological surprise—tailored specifically to the domain of AI-guided discovery in materials science. The key distinctions among these types are articulated along four core dimensions: the source of the surprise (originating in the AI model or in the human scientist), the trigger (prediction error, out-of-distribution data, contradiction with existing theory, or unexpected patterns in the inquiry process itself), the experiencer (primarily the model or the scientist), and the epistemic consequences that follow (model retraining, expansion of representational capacity, theory revision, or redesign of search and measurement strategies). By furnishing this conceptual framework, the paper offers clear implications for designing AI systems that can report, distinguish, and cultivate productive forms of surprise, thereby transforming AI from a passive predictor into an active partner in the discovery process and enabling more effective, targeted responses to different kinds of unexpectedness in materials science.