Specific learning disorders affect a significant portion of the population. A total of 80% of its instances are dyslexia, which causes significant difficulties in learning skills related to reading, memorizing and the exposition of concepts. Whereas great efforts have been made to diagnose dyslexia and to mitigate its effects at primary and secondary school, little has been done at the university level. This has resulted in a sensibly high rate of abandonment or even of failures to enroll. The VRAIlexia project was created to face this problem by creating and popularizing an innovative method of teaching that is inclusive for dyslexic students. The core of the project is BESPECIAL, a software platform based on artificial intelligence and virtual reality that is capable of understanding the main issues experienced by dyslexic students and to provide them with ad hoc digital support methodologies in order to ease the difficulties they face in their academic studies. The aim of this paper is to present the conceptual design of BESPECIAL, highlighting the role of each module that composes it and the potential of the whole platform to fulfil the aims of VRAIlexia. Preliminary results obtained from a sample of about 700 dyslexic students are also reported, which clearly show the main issues and needs that dyslexic students experience and these will be used as guidelines for the final implementation of BESPECIAL.

This study was designed to explore learning experiences of university students with dyslexia and factors that could contribute to their success in the university career. Although, great efforts have been made to diagnose dyslexia and to mitigate its effects at primary and secondary school, little has been done at the university level in particular in the Italian context. Indeed in the university context, the availability and possibility to use of support tools, that enable the student to achieve educational success, is still not sufficiently adequate. In this paper we used bivariate association tests and cluster analysis, in order to identify the most suitable compensatory tools and support strategies that can facilitate the students’ performance in higher education. The data were obtained through the voluntary participation of Italian students, enrolled in a bachelor degree course, with certified diagnosis of dyslexia. Six groups of students were identified from the cluster analysis, defining specific support tools and learning strategies for each group. Furthermore, through the creation of these six groups, it was possible to describe “profiles” that highlight the risk factors (late diagnosis) and-or protection factors (such as associations, support from friends and family) in analyzing the academic career of students with dyslexia. Therefore, starting from these data, through artificial intelligence it will be possible to identify and suggest study methodologies and create specific support tools for each student that can enable her/him to achieve educational success in her/his academic career.

This study was designed to explore learning experiences of university students with dyslexia and factors that could contribute to their success in the university career. Indeed, whereas great efforts have been made to diagnose dyslexia and to mitigate its effects at primary and secondary school, little has been done at the university level in particular in the italian context. In this paper we used bivariate association tests and cluster analysis, in order to identify the most suitable compensatory tools and support strategies that can facilitate the students’ performance. Data were obtained by voluntary participation of university students with dyslexia to an ad-hoc implemented questionnaire Results of our study reveal the importance of an earlier diagnosis and treatment for dyslexia with specific programs, in particular the support from association of dyslexic students has been shown as the most important one. Moreover, from the cluster analysis we identified six groups of students by defining specific support tools and learning strategies for each group. The findings have to be considered as the starting point for the implementation of artificial intelligence-based platform. In fact, the creation of dyslexia profiles can allow us to identify student-specific tools for supporting the academic career of dyslexic students.