The rapid expansion of cryptocurrency markets has introduced highly volatile, interconnected financial systems characterized by strong spillover and contagion effects across digital assets. These dynamics pose significant challenges not only for financial stability and risk management but also for engineering education, which often remains detached from real-world, data-driven financial systems. This study proposes a unified computational and experimental framework that examines cryptocurrency spillover dynamics while simultaneously addressing gaps in engineering education related to complex system modelling and practical problem solving. Using multi-asset cryptocurrency price data, the study employs vector autoregression, spillover index analysis, and network-based modelling to capture volatility transmission and systemic risk among major cryptocurrencies. The computational results are then embedded into an experimental learning framework designed for engineering students, where real-time market data and analytical tools are used to enhance systems thinking, uncertainty handling, and computational competence. The findings indicate that dominant cryptocurrencies act as primary transmitters of volatility, creating asymmetric spillover structures that resemble engineered networked systems. Educational experiments further reveal that exposure to such real-world financial dynamics significantly improves students’ analytical reasoning, interdisciplinary understanding, and problem-solving confidence. By bridging cryptocurrency analytics with engineering pedagogy, this work demonstrates how live financial systems can serve as effective learning laboratories, contributing to both advanced risk analysis and outcome-oriented engineering education..