# Use of recommendation models to provide support to dyslexic students

arXiv: https://arxiv.org/abs/2403.14710 · HTML: https://mlsanity.com/paper/2403.14710/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 63 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [Analysing the Effect of Recommendation Algorithms on the Amplification of Misinformation](https://mlsanity.com/paper/2103.14748/) (2021)
- [A VR Serious Game to Increase Empathy towards Students with Phonological Dyslexia](https://mlsanity.com/paper/2401.10926/) (2024)
- [Determining the Difficulties of Students With Dyslexia via Virtual Reality and Artificial Intelligence: An Exploratory Analysis](https://mlsanity.com/paper/2402.01668/) (2024)

## Built on by (most cited first, 2 of 2)

- [Fostering Inclusion: A Virtual Reality Experience to Raise Awareness of Dyslexia-Related Barriers in University Settings](https://mlsanity.com/paper/2502.15039/) (2025)
- [Combine Virtual Reality and Machine-Learning to Identify the Presence of Dyslexia: A Cross-Linguistic Approach](https://mlsanity.com/paper/2509.04510/) (2025)

## Abstract (arXiv)

Dyslexia is the most widespread specific learning disorder and significantly impair different cognitive domains. This, in turn, negatively affects dyslexic students during their learning path. Therefore, specific support must be given to these students. In addition, such a support must be highly personalized, since the problems generated by the disorder can be very different from one to another. In this work, we explored the possibility of using AI to suggest the most suitable supporting tools for dyslexic students, so as to provide a targeted help that can be of real utility. To do this, we relied on recommendation algorithms, which are a branch of machine learning, that aim to detect personal preferences and provide the most suitable suggestions. We hence implemented and trained three collaborative-filtering recommendation models, namely an item-based, a user-based and a weighted-hybrid model, and studied their performance on a large database of 1237 students' information, collected with a self-evaluating questionnaire regarding all the most used supporting strategies and digital tools. Each recommendation model was tested with three different similarity metrics, namely Pearson correlation, Euclidean distance and Cosine similarity. The obtained results showed that a recommendation system is highly effective in suggesting the optimal help tools/strategies for everyone. This demonstrates that the proposed approach is successful and can be used as a new and effective methodology to support students with dyslexia.