This technical report presents Cognitive Flexibility Theory, a framework for advanced knowledge acquisition in ill-structured domains. The theory emphasizes the need for multiple representations, schema assembly, and the centrality of cases in learning. It addresses the challenges of advanced learning, where conceptual complexity and ill-structuredness are prevalent. The theory suggests that traditional instructional methods, which often oversimplify and compartmentalize knowledge, are inadequate for advanced learning. Instead, it advocates for a more flexible approach that promotes active participation, tutorial guidance, and adjunct support to manage complexity. The report discusses the research findings from studies on medical students, highlighting the prevalence of misconceptions due to oversimplification and rigid thinking. These misconceptions are often reinforced by the way knowledge is presented and processed in introductory learning. The theory proposes that advanced knowledge acquisition requires a shift from rigid, precompiled knowledge structures to flexible, adaptable knowledge that can be applied in diverse contexts. Cognitive Flexibility Theory is implemented through computer hypertext systems that allow for multiple representations and flexible knowledge assembly. These systems enable learners to explore complex conceptual landscapes in various ways, promoting deeper understanding and application of knowledge. The theory also emphasizes the importance of cases in learning, as they provide real-world contexts for applying knowledge and understanding complex concepts. The report outlines the key themes of Cognitive Flexibility Theory, including the avoidance of oversimplification, the use of multiple representations, the centrality of cases, and the importance of active participation and tutorial guidance. These themes are supported by research findings and are implemented in computer hypertext systems designed to facilitate advanced knowledge acquisition in ill-structured domains. The theory provides a framework for developing more effective instructional methods that support deep learning and the application of knowledge in complex, real-world situations.This technical report presents Cognitive Flexibility Theory, a framework for advanced knowledge acquisition in ill-structured domains. The theory emphasizes the need for multiple representations, schema assembly, and the centrality of cases in learning. It addresses the challenges of advanced learning, where conceptual complexity and ill-structuredness are prevalent. The theory suggests that traditional instructional methods, which often oversimplify and compartmentalize knowledge, are inadequate for advanced learning. Instead, it advocates for a more flexible approach that promotes active participation, tutorial guidance, and adjunct support to manage complexity. The report discusses the research findings from studies on medical students, highlighting the prevalence of misconceptions due to oversimplification and rigid thinking. These misconceptions are often reinforced by the way knowledge is presented and processed in introductory learning. The theory proposes that advanced knowledge acquisition requires a shift from rigid, precompiled knowledge structures to flexible, adaptable knowledge that can be applied in diverse contexts. Cognitive Flexibility Theory is implemented through computer hypertext systems that allow for multiple representations and flexible knowledge assembly. These systems enable learners to explore complex conceptual landscapes in various ways, promoting deeper understanding and application of knowledge. The theory also emphasizes the importance of cases in learning, as they provide real-world contexts for applying knowledge and understanding complex concepts. The report outlines the key themes of Cognitive Flexibility Theory, including the avoidance of oversimplification, the use of multiple representations, the centrality of cases, and the importance of active participation and tutorial guidance. These themes are supported by research findings and are implemented in computer hypertext systems designed to facilitate advanced knowledge acquisition in ill-structured domains. The theory provides a framework for developing more effective instructional methods that support deep learning and the application of knowledge in complex, real-world situations.