Role of Artificial Intelligence and the Internet of Things in Education and Cultural Studies

Abstract AI and IoT add most value in education and cultural studies if they expand who is reached and protected, and our simulations suggest that they may narrow that reach almost as easily. This paper (i) synthesizes the literature on AI (Artificial Intelligence) and the Internet of Things (IoT) across teaching and learning, assessment, campus operations, digital humanities, and heritage preservation, and (ii) evaluates two representative applications with reproducible Monte Carlo models. The first is an early-warning system that directs limited tutoring capacity to at-risk students, with an explicit equity metric for a minority group whose data are less informative. The second is IoT microclimate monitoring in a heritage gallery with three alarm strategies. In a simulated cohort of 2,000 students (15% at risk, 300 tutoring slots), random allocation reached 15.2% of at-risk students, LMS analytics reached 37.4% and combining LMS and IoT data reached 50.2%. However, the minority group was reached 21.7 percentage points less often than the majority group. Allocating capacity by group quotas cut that gap to 15.7 points at the same overall reach (50.0%) and only equal data quality removed it. In the gallery simulation, a fixed humidity threshold detected 47.0% of excursions and raised 12.4 false alarms per 30 days, whereas a three-sensor consensus rule detected 98.0% with 0.7 false alarms. We conclude that data quality and fairness, not model accuracy alone, determine educational benefit, and that sensor redundancy matters more than a single smarter detector. We propose a four-principle governance framework covering equity, privacy, cultural sovereignty, and human oversight. All results are simulation-based and the full Python code is provided.