Integrating Sustainability, Biodiversity, and Human Health to Improve Indoor University Spaces: A Comparative Case Study Using Real-Time Environmental Monitoring and AI-Driven Analysis

Climate Change (CC) has increasingly disrupted the global environment, with significant consequences for biodiversity, cities, and human health and well-being over the past decades. Mitigating anthropogenic emissions and promoting healthier, more sustainable built environments are extremely vital. Despite growing research on CC, biodiversity, and public health, limited studies have integrated these interconnected issues using AI-assisted approaches to improve Indoor Environmental Quality (IEQ). This study aims to investigate the role of Indoor Finishing Materials (IFMs) in mitigating indoor emissions and enhancing occupants’ health, and well-being in university buildings. A mixed-methods approach (qualitative and quantitative) was employed, combining a literature review, a scientometric analysis, and a comparative case study of two educational campuses (New Giza University (NGU), Egypt, and the University of Pavia (UNIPV), Italy). Data were collected through a health-oriented questionnaire, based on PANAS and the WHO-5 Well-Being Index, to assess occupants’ perceptions and sensor-based monitoring of IEQ, particularly Indoor Air Quality (IAQ), and a biodiversity-related indicator, the View Nature Percentage (VNP), to quantify occupants’ seat-level visual access to outdoor greenery. Monitoring environmental readings was integrated into an AI-linked platform to support the continuous assessment of two selected classrooms. The survey results revealed that 39% of NGU respondents experienced headache and fatigue most of the time, compared with 21% at UNIPV. After normalising these concentrations to pollutants’ (CO2, VOCs, and NH3) mass per occupant and room volume—to control for the two classrooms’ differing size and occupancy rather than the raw ppm alone—NGU emitted more per occupant across all three pollutants. Despite the small sample (n = 32), a Spearman correlation analysis found 20 of 28 possible well-being symptom pairings to be statistically significant (p < 0.05), a pattern that would require a frankly large effect to arise by chance at this sample size. The study proposes sensor-informed guidelines integrated with an AI-enabled monitoring platform to ensure healthier indoor environments. This approach provides a practical framework for improving IEQ, reducing climate and health risks associated with harmful IFM emissions, and promoting sustainability, ecological well-being, and occupant productivity in educational buildings.