Artificial intelligence (AI) is reshaping education, scientific training, and materials discovery. In materials science, AI models increasingly support property prediction, experiment prioritization, and hypothesis generation; however, the limiting factor is no longer only algorithmic capability but also whether students and educators can use AI with domain-specific scientific judgment. This workshop-informed white paper and curriculum-oriented position article argues that AI education for AI-powered materials discovery must move beyond tool access and surface-level interaction with generative AI systems toward a workflow-aligned model of AI literacy. We connect AI literacy to materials-informatics competencies: data provenance, domain-specific featurization, model validation, uncertainty quantification, physics informed reasoning, reproducibility, and experimental feedback. We also emphasize outcome-oriented equity: institutions should evaluate not only access, participation, and engagement, but also whether AI-enabled instruction produces comparable learning gains, transfer of learning, confidence calibration, defined as the alignment with students confidence and the quality or correctness of their work, persistence, and research readiness across student subgroups. The paper synthesizes relevant evidence, identifies risks for learners such as cognitive off-loading and cognitive surrender, and provides a dual-track curriculum model and implementation recommendations such as curriculum guides and an assessment plan for courses, bootcamps, workshops, and program-level reform. The central goal is to prepare students to become better scientists, not merely more efficient users of AI tools.

翻译：人工智能（AI）正在重塑教育、科学训练和材料发现。在材料科学领域，AI模型日益支持性质预测、实验优先级排序和假设生成；然而，制约因素已不再仅仅是算法能力，还在于学生和教育者能否将AI与领域特定的科学判断相结合。这份基于研讨会的白皮书及课程导向的立场文章提出，面向AI驱动的材料发现的AI教育必须超越工具接入和与生成式AI系统的表层交互，转向工作流对齐的AI素养模型。我们将AI素养与材料信息学能力相关联：数据溯源、领域特定特征化、模型验证、不确定性量化、物理信息推理、可重复性和实验反馈。我们还强调以结果为导向的公平性：机构应评估的不仅是接入、参与和互动，还包括AI赋能教学能否在不同学生子群体中产生可比较的学习增益、学习迁移、自信校准（定义为学生自信与工作质量或正确性的一致性）、坚持度和研究准备度。本文综合相关证据，识别认知卸载和认知投降等学习者风险，并提供双轨课程模型及实施建议，如课程指南、评估计划，适用于课程、训练营、研讨会及项目层面改革。核心目标是培养学生成为更优秀的科学家，而不仅仅是AI工具的高效使用者。