The symbiotic campus: integrating physical and digital realities through AI, IoT and digital twins
Year: 2026
Type: article
Abstract: Universities occupy, operate, and maintain substantial physical estates, yet the digital and physical dimensions of campus life remain largely disconnected. Building management, timetabling, space utilisation, energy procurement, and learning delivery operate as separate systems with incompatible data models, producing structural inefficiency and foreclosing pedagogical possibilities that depend on continuous coupling between physical context and digital capability. This paper advances a reference architecture for the symbiotic campus-an institution in which a real-time digital twin of the physical estate is continuously synchronised with sensor telemetry, occupancy data, energy systems, and learning platforms, and in which AI-driven management systems act upon that twin to optimise energy, space, and pedagogy simultaneously. The architecture comprises seven layers: a sensing layer, a connectivity layer, a semantic integration layer, a digital twin model layer, an AI orchestration layer, an experience layer, and a cross-cutting governance plane. The paper then presents an embedded case study of implementation at a mid-sized European technical university over thirty months, documenting energy reduction of 21.4%, space utilisation improvement of 34%, and measurable hybrid-learning gains, alongside five significant challenges: sensor calibration drift, semantic interoperability failure, legacy building management system integration, privacy controversy over occupancy sensing, and a persistent governance capacity deficit. The paper argues that the symbiotic campus is technically achievable with mature components but institutionally demanding, and that the binding constraints are semantic and political rather than computational.
Institution: University of Travnik
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225728093
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