Adaptive Human–Computer Interaction Frameworks for Intelligent Learning Environments Using AI and Eye-Tracking Analytics

Artificial Intelligence (AI) and Human–Computer Interaction (HCI) are converging to redefine the landscape of intelligent learning environments. Adaptive HCI frameworks integrate advanced AI algorithms with real-time eye-tracking analytics to personalize user experiences, enabling systems to dynamically interpret and respond to learners’ cognitive states, attention span, and engagement levels. These frameworks aim to move beyond static e-learning models by developing responsive educational systems that learn from user behavior, visual focus, and interaction data to optimize instructional delivery. Modern learning systems are increasingly designed with embedded analytics, natural language interfaces, and visual sensors, creating seamless, data-driven learning environments. Predictive modeling and machine learning algorithms play a critical role in interpreting gaze data, inferring comprehension levels, and automating adaptive feedback loops. This study explores the integration of AI-powered gaze tracking within intelligent learning interfaces, presenting a comparative evaluation of adaptive versus traditional static systems. The findings highlight that adaptive HCI frameworks improve knowledge retention, engagement, and accessibility while enabling educators to make data-informed pedagogical decisions. The ongoing evolution of intelligent, emotion-aware, and adaptive systems positions HCI as a transformative force in the design of next-generation learning ecosystems.