Instructor-centered case generation with GenAI: a design-based exploration

Purpose The purpose of this study is to explore how generative artificial intelligence (GenAI) can support instructors in designing pedagogically complex teaching materials, specifically customizable case studies – a largely underexplored area in current educational research, which has predominantly focused on student-facing applications. Design/methodology/approach Adopting a design-based research approach, this study implemented a mixed-methods exploratory case design grounded in the Technology Acceptance Model and Cognitive Load Theory. Data sources included system usage analytics, service interaction logs and qualitative analysis of AI-generated instructional content. Findings Instructors demonstrated increasing engagement with the GenAI-powered tool, suggesting positive perceptions of its usefulness and ease of use. The tool also helped reduce instructors’ cognitive load by automating case structure and aligning outputs with teaching objectives. Emergent themes highlighted efficiency, customization and pedagogical integration. Research limitations/implications Several limitations must be acknowledged. First, while usage data is rich in behavioral detail, it lacks user sentiment and rationale, which could be captured through future interviews or surveys. Second, this study focused on early-stage adoption, and longitudinal data would be valuable in evaluating sustained usage and integration. Third, the pilot involved a self-selected sample of early adopters; generalizing to broader populations requires replication in diverse institutional settings. Practical implications Future research should explore hybrid data collection models that combine log data with instructor reflections and student outcome measures. Comparative studies of different GenAI tools, as well as domain-specific adaptations (e.g. for health or engineering education), would also provide valuable insights. Additionally, as GenAI tools evolve to include multimodal and multilingual capabilities, future work should examine how these affect cognitive load and usability. Originality/value This study shifts the focus from student-facing AI tools to instructor-centered applications, offering novel insights into how GenAI can serve as a co-design partner in curriculum development. This study contributes to theory by applying Technology Acceptance Model and Cognitive Load Theory to instructor-facing GenAI use in higher education and highlights key ethical and institutional considerations for responsibly scaling AI-driven instructional design.