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.

翻译：阅读障碍是最普遍的特殊学习障碍，会显著损害不同的认知领域。这进而对阅读障碍学生的学习过程产生负面影响。因此，必须为这些学生提供专门的支持。此外，由于该障碍所引发的问题可能因人而异，这种支持还必须高度个性化。在本研究中，我们探讨了利用人工智能为阅读障碍学生推荐最合适支持工具的可能性，从而提供具有实际效用的针对性帮助。为此，我们采用了推荐算法——机器学习的一个分支，旨在识别个人偏好并提供最合适的建议。我们实现并训练了三种协同过滤推荐模型，即基于物品的模型、基于用户的模型和加权混合模型，并在一个包含1237名学生信息的大型数据库上研究了它们的性能，该数据库通过一份关于所有最常用支持策略和数字工具的自评问卷收集而来。每种推荐模型均使用三种不同的相似性度量进行测试，即皮尔逊相关性、欧几里得距离和余弦相似度。获得的结果表明，推荐系统在为每个人推荐最佳帮助工具/策略方面非常有效。这证明了所提出的方法是成功的，并可以作为支持阅读障碍学生的一种新的有效方法论使用。