AI Eye-Tracking Technology: A New Era in Managing Cognitive Loads for Online Learners

Abstract

Eye-tracking technology has emerged as a valuable tool for evaluating cognitive load in online learning environments. This study investigates the potential of AI-driven consumer behaviour prediction eye-tracking technology to improve the learning experience by monitoring students’ attention and delivering real-time feedback. In our study, we analysed two online lecture videos used in higher education from two institutions: Oxford Business College and Utrecht University. We conducted this analysis to assess cognitive demands in PowerPoint presentations, as this directly affects the effectiveness of knowledge dissemination and the learning process. We utilised a neuromarketing-research consumer behaviour eye-tracking AI prediction software called ‘Predict’, which employs an algorithm constructed on the largest neuroscience database (comprising previous studies conducted on live participants n = 180,000 with EEG and eye-tracking data). The analysis for this study was carried out using the programming language R, followed by a series of t-tests for each video and Pearson’s correlation tests to examine the relationship between ocus and cognitive demand. The findings suggest that AI-powered eye-tracking systems have the potential to transform online learning by providing educators with valuable insights into students’ cognitive processes and enabling them to optimise instructional materials for improved learning outcomes.