A Systematic Literature Review of Eye-Tracking and Machine Learning Methods for Improving Productivity and Reading Abilities

Abstract

Deteriorating eyesight is increasingly prevalent in the digital age due to prolonged screen exposure and insufficient eye care, leading to reduced productivity and difficulties in maintaining focus during extended reading sessions. This systematic literature review, following PRISMA guidelines, evaluates 1782 articles, with 42 studies ultimately included, assessing their quality using the Mixed Methods Appraisal Tool (MMAT). The selected studies are categorised into eye metric classification, measuring comprehension, measuring attention, and typography and typesetting. Recent advances have demonstrated the potential of machine learning to enhance eye movement predictions, such as the classification of fixations and saccades, while other research utilises eye metrics to assess mental fatigue and attention levels. Additionally, modifications to typography have been explored as a means of improving focus and memory retention. The findings highlight the transformative role of eye-tracking technologies and machine learning in understanding reading behaviour, attention, and cognitive workload. However, challenges such as data scarcity, limited generalisability, and biases in existing methodologies persist. Addressing these gaps through standardised frameworks, diverse datasets, and advancements in synthetic data generation could enhance the accessibility, accuracy, and real-world applicability of eye-tracking solutions for improving reading comprehension and focus.