This paper proposes an innovative methodology for enhancing museum accessibility and inclusivity for visitors with specific learning disorders (SLDs) using audio-visual interfaces based on patented technology. The approach involves analyzing user preferences and dyslexic students’ self-assessments through two questionnaires. This study gathered 126 responses from both SLD-certified individuals and those without intellectual disabilities for the first questionnaire and over 1300 responses exclusively from SLD-certified individuals for the second. Results suggest practical solutions such as new visual effects, gamification methods, and user-friendly informational materials linked to an AI system. These findings serve as guidelines for developing technology to improve museum accessibility, particularly for individuals with SLDs.

This study explores the use of virtual reality (VR) and artificial intelligence (AI) to predict the presence of dyslexia in Italian and Spanish university students. In particular, the research investigates whether VR-derived data from Silent Reading (SR) tests and self-esteem assessments can differentiate between students that are affected by dyslexia and students that are not, employing machine learning (ML) algorithms. Participants completed VR-based tasks measuring reading performance and self-esteem. A preliminary statistical analysis (t-tests and Mann–Whitney tests) on these data was performed, to compare the obtained scores between individuals with and without dyslexia, revealing significant differences in completion time for the SR test, but not in accuracy, nor in self-esteem. Then, supervised ML models were trained and tested, demonstrating an ability to classify the presence/absence of dyslexia with an accuracy of 87.5% for Italian, 66.6% for Spanish, and 75.0% for the pooled group. These findings suggest that VR and ML can effectively be used as supporting tools for assessing dyslexia, particularly by capturing differences in task completion speed, but language-specific factors may influence classification accuracy.