Abstract:

Traditional business case studies rely on text-heavy formats that fail to leverage the demonstrated benefits of visual representation for learning and comprehension, while providing limited scaffolding for analyzing the complex multi-stakeholder perspectives that characterize contemporary business decisions. Rather than incremental improvements, these documented limitations require systematic transformation. The emergence of generative artificial intelligence tools creates opportunities to demonstrate how AI can serve as a design partner for educators, enabling visualizations that clarify complex organizational challenges and role-specific analytical activities that connect students meaningfully to authentic organizational decision-making contexts.

The main contribution of our research is an AI-Assisted Case Study Transformation Framework, which enables faculty to enhance traditional case materials through two key innovations: AI-generated process visualizations that reduce cognitive burden while clarifying organizational relationships, and role-differentiated data analytics that scaffold learning within distinct stakeholder perspectives. This framework draws from cognitive load theory and multimedia learning principles, showing how these established pedagogical foundations can be used through AI-assisted content design. The approach aligns with constructivist learning principles and contemporary scaffolding research, while incorporating recent advances in multimodal learning to promote cognitive performance and learning engagement.

We demonstrate the framework through case studies where AI-generated process visualizations clarify complex organizational challenges, while role-differentiated analytics enable students to engage with identical datasets from distinct stakeholder perspectives.

The framework's reproducibility stems from its systematic methodology: educators can apply the same transformation process across diverse case types, from financial decision-making scenarios to healthcare management contexts, without requiring technical AI expertise. This approach moves beyond AI as merely a content delivery mechanism, offering educators a practical methodology for creating visually engaging, analytically rigorous case materials that maintain the authentic decision-making complexity essential for business education. This research contributes a theoretically grounded, practically replicable approach to AI-enhanced case pedagogy, providing a foundation for empirical validation across multiple educational contexts and disciplines.

Keywords:

Cognitive load theory, Multi-stakeholder pedagogy, Process visualization, Generative AI, Case study transformation, Scaffolding.