An AI-powered digital twin framework for smart campus management: A Conceptual Model for Częstochowa University of Technology

The growing complexity of university campus operations, coupled with rising energy costs and sustainability demands, necessitates intelligent, data-driven management solutions. Digital Twin (DT) technology, which creates a dynamic virtual replica of a physical system, has emerged as a transformative approach for optimising infrastructure in industrial and commercial environments. However, its application within higher education institutions (HEIs) in Central and Eastern Europe remains largely unexplored. This paper proposes a five-layer AI-powered Digital Twin framework conceptually tailored to Częstochowa University of Technology (CUT), Poland. The framework integrates a data acquisition layer, a middleware integration layer, an artificial intelligence layer employing Long Short-Term Memory (LSTM) networks for energy forecasting, Random Forest classifiers for predictive maintenance, and K-Means clustering for space utilisation analysis, a virtual twin simulation layer, and a decision-support layer comprising management dashboards and automated alert systems. The proposed architecture is evaluated theoretically against existing DT frameworks in the literature, demonstrating its scope, AI integration depth, and contextual relevance for Polish technical universities. Projected benefits include 15–25% energy reduction, 30–40% decrease in unplanned downtime, and 10–20% improvement in space utilisation. The paper concludes with a phased 18-month implementation roadmap and directions for future empirical validation. This work provides a replicable conceptual foundation for smart campus transformation across HEIs in Poland and the broader Central European region.