This study examines the adoption, effectiveness and challenges of AI (Artificial Intelligence) and IoT (Internet of Things) technologies to improve energy efficiency in student housing, with a focus on comparing regions in the Global North and Global South.
The research is based on a systematic review of 42 peer-reviewed articles published between 2020 and 2025. Of these, 28 studies on the Global North and 14 on the Global South met the criteria. The review highlights patterns in energy savings, its scope and usage, and the challenges encountered during implementation.
Adoption rates of AI and IoT in student housing are notably higher in the Global North than in the South. The findings show that the Global South has below 30% influence due to cost, infrastructure and digital literacy constraints. AI models consistently demonstrated effectiveness, with predictive accuracies between 85% and 95% and IoT energy savings of 15%–30%. The Global South witnessed technical integration challenges, long-term maintenance and behavioural resistance. High upfront costs, an unreliable power supply, together with insufficient institutional expertise, posed challenges for the Global South.
These findings require more inclusive AI and IoT energy systems tailored to different regions. Affordability, user engagement and maintenance capacity must be balanced in these strategies to ensure the successful integration of AI and IoT in student housing energy savings.
The systematic comparison of AI and IoT adoption in student housing provides quantitative evidence of effectiveness and reveals region-specific barriers.
