INTEGRATION OF LEAN CANVAS INTO THE TRAINING OF IT SPECIALISTS AS A MECHANISM FOR COGNITIVE SIMPLIFICATION AND RATIONALIZATION OF COMPLEX SOLUTIONS IN THE IT PRODUCT LIFECYCLE

Abstract

This study is devoted to the integration of the Lean Canvas tool into the training of IT specialists as a mechanism for cognitive simplification and rationalization of complex decisions at the early stages of the digital product life cycle. The theoretical basis is the approaches of hypothesis-driven design and experimental verification of assumptions, which allow minimizing uncertainty and resource-intensive errors in student teams. The aim of the article is to conceptualize the role of Lean Canvas in the educational process in the specialties of "Computer Science" and "Computer Engineering" and to substantiate the methodology of its pedagogical integration into the logic of product management (ideation - MVP - product/market fit). The methodology consists of analysis and synthesis of scientific sources, systemic and activity approaches, modeling of educational artifacts (rubrics, checklists, experiment templates) and empirical testing within curricula focused on team project work. The operational framework for applying Lean Canvas in higher education is presented: mapping of risk hypotheses (problem, segments, value, channels, metrics), forming a test plan, linking with engineering practices (requirements management, CI/CD, UX research), as well as a set of performance indicators (problem-solution fit, conversions, engagement). The scientific novelty lies in the combination of epistemological analysis with the practices of organizational learning of student teams and in the introduction of verified success criteria for educational interventions based on Lean Canvas. The practical significance lies in creating a reproducible methodology for integrating this tool into educational modules on entrepreneurship, product management, and software engineering, ensuring transparency of decisions, traceability of artifacts, and rapid closure of feedback loops. The results can be used to update educational programs, improve the quality of student IT projects, and implement evidence-based innovation management practices in the university environment.