This article presents a structured methodology for developing student competencies through the systematic use of software tools capable of generating and reinitializing repetitive problem situations grounded in students' prior knowledge. The core premise is that meaningful competency formation requires repeated, contextually varied encounters with problem types that activate and build upon existing cognitive schemas. Drawing on principles of spaced repetition, adaptive scaffolding, and problem-based learning, the proposed methodology integrates intelligent tutoring systems, scenario-generation platforms, and learning management systems to produce personalized, cyclically refreshed problem sets. An experimental study conducted at Asia International University, Bukhara, involving 62 undergraduate students across two cohorts, demonstrated that the proposed approach yields a normalized learning gain of g = 0.64 in technical problem-solving competencies, significantly outperforming the conventional curriculum (g = 0.29). The article discusses the architectural requirements for competency-initializing tools, their integration into the academic workflow, and recommendations for practical adoption in higher education institutions.