Designing Inclusive Hybrid Learning Using Eye-Tracking and Adaptive UX: A Neuroadaptive Framework

Background. The growing diversity of learners in hybrid education environments necessitates adaptive systems that respond to individual cognitive and emotional states in real time. Traditional user experience (UX) models often fail to accommodate neurodivergent users or those with varying attention patterns and processing styles. Purpose. This study proposes a neuroadaptive framework that integrates eye-tracking technology and adaptive UX design to create inclusive hybrid learning experiences. Method. The research aims to examine how real-time gaze data can inform interface adjustments that support engagement, accessibility, and cognitive load management. Employing a mixed-method design, the study involved 58 university students who interacted with a prototype learning platform embedded with eye-tracking sensors and adaptive UX features. Quantitative data from gaze patterns, task completion, and performance metrics were complemented by qualitative feedback through user interviews and think-aloud protocols. Results. Results indicate that the neuroadaptive interface significantly improved task efficiency, learner focus, and subjective usability across diverse cognitive profiles. Conclusion. The study concludes that This study demonstrates that real-time biometric feedback can personalize hybrid learning experiences and improve inclusivit.