## 1 Introduction  
Educational institutions worldwide contend with exponential student enrolment, limited budgets, inappropriate digital learning content, and elevated performance expectations, which point further to the need for more efficient administrative models for their development [1, 2]. Furthermore, an effective and smart development requires not only technological infrastructure but also alignment with sustainable development goals, aiming for environmental sustainability, social responsibility, and comprehensive user engagement [3]. Conventional processes for scheduling courses, assigning resources, and preserving campus facilities typically rely on manual or siloed systems, creating pervasive scheduling conflicts, underutilised classrooms, and burdensome administrative coordination [4]. For instance, schools often endure last-minute classroom reassignments because of poorly updated timetables and a lack of cohesive resource-monitoring tools [5]. These inefficiencies not only generate operational bottlenecks but can also degrade student learning experiences and inflate institutional costs [6]. The recent advancements in Artificial Intelligence (AI) can deliver on-to-the-point predictive analytics and intelligent decision support to the institutions, fueled by diverse data streams [7]. Simultaneously, the Internet of Things (IoT) equips campuses with a continuously growing network of interconnected sensors and devices, facilitating real-time collection of occupancy levels, environmental conditions, and equipment performance data. When coupled with next-generation wireless networks, such as 5G, high throughput can be harnessed, as well as massive device capacity, live adaptive service, and ultra-reliable, low-latency connections for rapid data sharing and prompt decision execution [8; 9; 10]. This synergy enables, for example, dynamic resource allocation, based on real-time usage patterns, and more proactive upkeep of essential infrastructure through predictive maintenance algorithms [11]. As a natural evolution of these converging technologies, the notion of the \"smart campus\" has steadily gained traction [12]. Drawing inspiration from the concept of \"smart cities\", smart campuses aim to merge AI-driven analytics, IoT-enabled data collection, and cloud-based systems in an integrated ecosystem which optimises campus operations and enriches user experiences [13]. Such an ecosystem can manage real-time scheduling by cross-referencing class attendance data, building occupancy rates, and instructor availability to propose or automatically implement course reassignments. In addition, the automated provisioning of resources--for instance, air-conditioning or security cameras and power--becomes more precise with continuous sensor input, potentially reducing operational costs [5]. Implementations of administrative models indicate that individual AI or IoT elements can simplify singular tasks. For example, automated attendance tools often combine camera-based analytics with IoT devices to track and record student presence seamlessly, obviating the need for traditional paper-based sign-ins [12; 14; 15]. Similarly, sensor-driven reservation systems can handle frequent requests to book lab space, meeting rooms, or equipment [6], significantly diminishing administrative overhead and cutting response times. Yet, many institutions which deploy such solutions do so in an ad hoc manner; one department might adopt an IoT-based lighting system, while another relies on an AI scheduling tool, resulting in fragmented or duplicated efforts [4; 16; 17]. Fully harnessing the potential of AI and wireless IoT in education demands a comprehensive framework, which supports interaction among multiple campus processes, from timetabling and asset lifecycle management to data-driven policy decisions. This broader integration also foregrounds vital concerns regarding data privacy, cybersecurity, and ethical deployment.