<![CDATA[This study aimed to develop and validate AI–CBL learning materials for solid-state physics that were designed to support pre-service physics teachers’ representational competence. A research and development approach using the ADDIE framework was applied and limited to the Analysis, Design, and Development stages. The analysis stage identified key learning difficulties and representational demands in solid-state physics instruction for pre-service physics teachers. Based on these findings, an AI-integrated Case-Based Learning package was designed and developed, consisting of contextual case studies, structured learning tasks aligned with case-based learning phases, and guidelines for responsible interaction with open-access generative artificial intelligence. The materials were organized through a learning management system to support consistent access, structured sequencing, and documentation of learning artifacts. The developed materials were reviewed by three expert validators using the Learning Object Review Instrument (LORI) 2.0 across eight aspects on a five-point scale. The validation data were analyzed descriptively and supported by Many-Facet Rasch Measurement using Minifac to examine rater consistency and measurement quality. The expert validation results showed an average feasibility percentage of 76.50% across the eight LORI aspects, indicating that the developed materials were feasible for further implementation. Rasch-based analysis also indicated good unidimensionality, with 47.84% of variance explained by Rasch measures, and acceptable rater consistency, with reliability of 0.87. Revisions were made based on validators’ comments to improve clarity, learning-goal alignment, usability, and responsible artificial intelligence integration. These findings indicate that the developed AI–CBL learning materials are feasible as a validated instructional product and provide a foundation for subsequent classroom implementation studies in solid-state physics.]]>
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