Determining the Difficulties of Students With Dyslexia via Virtual Reality and Artificial Intelligence: An Exploratory Analysis

TL;DR

The paper addresses the challenge of supporting university students with dyslexia, a group underserved by higher-education resources. It proposes VRAIlexia, an integrated VR- and AI-based framework comprising a VR mobile app for psychometric testing and an AI engine to predict personalized study tools and learning strategies using VR data and multilingual surveys. The AI models achieve mean accuracies around 90% for predicting tools and strategies, demonstrating strong potential for tailoring dyslexia accommodations in HEIs. This approach offers a scalable path to systematically identify effective supports, potentially reducing dropout and improving academic outcomes for dyslexic students in higher education.

Abstract

Learning disorders are neurological conditions that affect the brain's ability to interconnect communication areas. Dyslexic students experience problems with reading, memorizing, and exposing concepts; however the magnitude of these can be mitigated through both therapies and the creation of compensatory mechanisms. Several efforts have been made to mitigate these issues, leading to the creation of digital resources for students with specific learning disorders attending primary and secondary education levels. Conversely, a standard approach is still missed in higher education. The VRAIlexia project has been created to tackle this issue by proposing two different tools: a mobile application integrating virtual reality (VR) to collect data quickly and easily, and an artificial intelligencebased software (AI) to analyze the collected data for customizing the supporting methodology for each student. The first one has been created and is being distributed among dyslexic students in Higher Education Institutions, for the conduction of specific psychological and psychometric tests. The second tool applies specific artificial intelligence algorithms to the data gathered via the application and other surveys. These AI techniques have allowed us to identify the most relevant difficulties faced by the students' cohort. Our different models have obtained around 90\% mean accuracy for predicting the support tools and learning strategies.

Figures (2)

• Figure 3: Achieved CCR for predicting the needed supporting tools.
• Figure 4: Achieved CCR for predicting the needed learning strategies.