The researcher presents a comprehensive investigation into cognitive architectures for adaptive problem-solving with specific application to computer science education. This study develops and evaluates a computational model that simulates the acquisition of expert knowledge in programming and algorithmic reasoning. Through rigorous experimentation and analysis, the researcher identifies key mechanisms that facilitate the transformation of cognitive structures during skill development. The results demonstrate significant advancements in understanding how novice programmers transition to expert status, revealing distinct cognitive patterns that emerge during this progression. These patterns include the formation of specialized mental schemas, the development of chunking mechanisms for efficient information processing, and the emergence of sophisticated heuristic strategies. The findings contribute to both theoretical understanding of expert cognition and practical applications in computer science pedagogy, offering evidence-based approaches for designing educational interventions that accelerate expertise development.