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Advancing Dyslexia Assessment in Children Through Computerized Testing
09:00

Advancing Dyslexia Assessment in Children Through Computerized Testing

Published on: August 16, 2024

AI-enabled eye-movement and emerging multimodal frameworks for precision dyslexia screening and reading pattern

Ashit Kumar Dutta1, Moattar Raza Rizvi2, Farha Mujeeb Ahmed Shaikh3,4

  • 1Department of Computer Science and Information Systems, College of Medical Sciences, AlMaarefa University, Dariyah, Saudi Arabia.

Frontiers in Medicine
|July 6, 2026
PubMed
Summary

Eye-tracking and machine learning offer scalable dyslexia screening by analyzing reading behaviors. These computational methods show high accuracy, aiding early identification and intervention for developmental dyslexia.

Keywords:
automated screeningdyslexiaelectrooculographyeye movementeye trackingmachine learningreading behavior

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Area of Science:

  • Neuroscience
  • Computational Linguistics
  • Biomedical Engineering

Background:

  • Developmental dyslexia impairs reading accuracy and fluency, necessitating early identification for intervention.
  • Traditional dyslexia assessments are time-consuming and resource-intensive, limiting scalability.
  • Eye-tracking and machine learning provide objective, data-driven alternatives for dyslexia screening.

Purpose of the Study:

  • To integrate evidence on eye-movement-based and multimodal computational methods for dyslexia screening.
  • To assess their utility in risk identification and algorithmic classification during reading tasks.
  • To evaluate the current state and limitations of these emerging technologies.

Main Methods:

  • Systematic literature search of PubMed, Scopus, Web of Science, and CINAHL (2015-2026).
  • Inclusion of studies analyzing eye-movement data (eye tracking or EOG) with or without predictive modeling.
  • Assessment of methodological quality using established tools (JBI, PROBAST, ROBINS-I, COSMIN).

Main Results:

  • Twenty-three studies were included, focusing on eye-movement biomarkers, machine learning models, and intervention responses.
  • Dyslexic readers consistently showed longer fixations, more regressions, and reduced saccadic efficiency.
  • Machine learning models achieved 80-95% classification accuracy, with some nearing 99%.

Conclusions:

  • Eye-movement-based computational systems represent a promising, non-invasive method for scalable dyslexia screening.
  • Significant heterogeneity exists in datasets and methodologies, requiring cautious interpretation of results.
  • Multimodal approaches are emerging but require further development and validation beyond gaze-derived features.