Abstract
Inclusive special education classrooms require dynamic environmental adjustments to accommodate students with diverse neurodevelopmental needs, such as Autism Spectrum Disorder (ASD) and Sensory Processing Sensitivity. Standard classroom environments often present sensory overstimulation—including erratic lighting, acoustic fluctuations, and sub-optimal thermal conditions—which can severely impair student engagement and trigger behavioral stress. This study presents the design, implementation, and usability evaluation of an Internet of Things (IoT)-enabled Adaptive Environmental Control System (AECS) tailored specifically for inclusive educational environments. The system utilizes a distributed network of environmental sensors (microphones, ambient light transducers, and microclimate sensors) coupled with edge-computing microcontrollers to autonomously regulate lighting intensity, color temperature, and ambient soundscapes, while offering intuitive teacher override capabilities via a centralized web dashboard. A mixed-methods usability evaluation was conducted across four special education settings over a twelve-week intervention period, involving 18 special education educators and 42 students. System usability was evaluated using the System Usability Scale (SUS), alongside qualitative observational logging of student sensory overload incidents. Results demonstrated high system usability (mean SUS score of 84.2 ± 5.6) and a statistically significant 37.4% reduction in sensory-driven disruptive behavioral incidents. These findings suggest that IoT-driven environmental adaptation can successfully alleviate sensory barriers, fostering more supportive and accessible learning environments for neurodivergent learners.
