The digitalization of vocational education requires scientifically grounded approaches to assess learning outcomes resulting from the integration of artificial intelligence, simulation tools, and Learning Management Systems (LMS). In response to the rapid evolution of digital technologies and the need for efficient resource utilization, this study proposes a mathematical model for evaluating the effectiveness of digital tools in training future professionals. The model is based on a system of weighted performance indicators: knowledge quality (Kq), learning time (Kt), student engagement (Ka), and resource expenditure (C). These indicators are synthesized into a composite efficacy index (E), enabling comparative analysis between digital and traditional instructional approaches. The research methodology involved analyzing data from LMS platforms such as Moodle and Google Classroom, as well as AI-based interactive environments. Student engagement was measured through analytical surveys, while instructional scenarios and cognitive load were modeled to assess learning dynamics. Generative AI tools were used to analyze student responses, identify semantic patterns, and refine indicator structures. Data normalization, statistical filtering, and expert-based weighting ensured the model’s validity. The model integrates quantitative metrics (e.g., scores, time, cost) with qualitative indicators (e.g., engagement, cognitive interaction), offering a comprehensive framework for evaluating educational outcomes. Validation was conducted through a comparative study involving two cohorts: one using digital technologies and another following traditional methods. Results demonstrated improved knowledge acquisition, a 22% increase in student activity, reduced learning time, and lower resource consumption in the digital cohort. The composite efficacy index (E) showed a 19% increase compared to the traditional model. This model provides a robust foundation for strategic decision-making in the digitalization of vocational education. Its application supports enhanced training quality, efficient resource allocation, and the development of adaptive, data-driven educational systems.