A workflow-aligned framework for AI literacy in materials-informatics education: scientific judgment, equity, and curriculum design

Artificial intelligence (AI) is increasingly used to predict materials properties, prioritize experiments, and generate hypotheses in materials science. The limiting challenge is no longer only algorithmic capability, but also whether students and educators can use AI with domain-specific scientific judgment. This conceptual analysis argues that AI education for AI-powered materials discovery should move beyond tool access and surface-level interaction with generative systems toward a workflow-aligned model of AI literacy. The analysis links AI literacy to core materials-informatics competencies: data provenance, domain-specific featurization, model validation, uncertainty quantification, physics-informed reasoning, reproducibility, and experimental feedback. It also advances an outcome-oriented equity perspective in which institutions evaluate not only access and participation, but also comparable learning gains, transfer of learning, learner confidence calibration, persistence, and research readiness across student subgroups. The article synthesizes relevant evidence, identifies cognitive and ethical risks such as off-loading and cognitive surrender, and proposes a dual-track curriculum model, implementation recommendations, reusable curriculum artifacts, and a set of testable propositions for future study. The central goal is to prepare students to become better scientists—not merely more efficient users of AI tools.