Ensuring equitable participation for students with invisible disabilities in educational environments remains a persistent and complex challenge. While progress has been made in providing assessments and accommodations, many existing approaches rely on reactive support mechanisms that do not fully address the dynamic and often unpredictable nature of certain conditions, such as epilepsy. In increasingly digital and technology-rich learning environments, there is a growing need for proactive, responsive, and integrated assistive systems that support both their safety and autonomy.

This paper presents a proof-of-concept study exploring an AI-enabled assistive ecosystem that integrates wearable sensing, machine learning, and trained assistance animals to support safe and independent participation in education. A smart collar equipped with motion sensors is used to detect trained alert behaviours in assistance dogs. When triggered, the system sends a warning to a caregiver or support contact via a mobile device, indicating that a seizure may be imminent and providing the individual’s location. Together, these elements suggest a pathway toward multimodal detection systems capable of enabling early alerts and timely intervention.

This work contributes to the development of hybrid human–animal–AI assistive ecosystems that extend beyond traditional accommodation models, demonstrating how multimodal approaches can support inclusive participation in educational contexts(Zawacki-Richter et al. 2019). Although preliminary and tested under controlled conditions, the study establishes the feasibility of intelligent assistive systems aligned with ongoing digital transformation in education.