The advent and the wide spread of information technology (IT) has also positively impacted the problem of dyslexia diagnosis and novel and interesting approaches exploiting digital technologies have been proposed. These can be broadly grouped into two categories [8]: The first one comprises those approaches based on neurological data analysis, which aim at spotting the differences in brain anatomy, organization and functioning that correlate with the presence of the typical symptoms caused by dyslexia [9; 10] by employing modern screening techniques and novel algorithms to enhance their output. Significant evidence of anomalies in dyslexic people's cerebral morphology and operative processes has been found through the analysis of 3D scans of the brain obtained with magnetic resonance imaging (MRI) [11; 12; 13; 14; 15] and functional magnetic resonance imaging (fMRI) [16; 17; 18; 19], respectively. In addition, different behaviors between dyslexic and non-dyslexic subjects have been observed in the frequency [20], entropy [21] and spatial activation patterns [22; 23] of electroencephalogram (EEG), a widely used technique to assess human concentration [24]. Interesting results have also been obtained with the tracking of the movements of the eye during the act of reading (easily and cheaply performable at the state-of-the art level thanks to the progress in the design and production of eye-trackers [25] and tracking techniques [26; 27; 28]), which have demonstrated that the saccades of the readers differ in number and amplitude, depending on if they have dyslexia or not [29; 30; 31; 32]. The second category consists of those approaches that revise and improve classical testing methodologies, using the potential offered by IT. These, in turn, can be divided into three groups, on the basis of the aspects they aim at improving: administration of the tests, choice of the most predictive ones and analysis of the results. The first group is focused on presenting the most consolidated tests for dyslexia diagnosis by means of platforms or digital tools that allow the facilitation and speeding up of the collection of the necessary data [33; 34; 35; 36]. These platforms/tools can also provide specialists with real-time information and help them monitor the specific objectives step by step, in order to constantly provide the chance to compare results and revise previous evaluations, increasing the probability of a correct final diagnosis [33]. In addition, their capability to gather and store data can also be exploited for research purposes [35]. This kind of approach is particularly useful for the assessment of dyslexia during childhood, since it allows the administration of tests as a set of serious games that are appealing to the children and hide the sensation of being under evaluation [33; 34; 35]. In light of its peculiar features, virtual reality (VR) has proven to be a powerful tool to achieve this goal and, in fact, its use in this area is increasing [36; 37]. VR also provides a control environment that submerges the user in a controlled but relaxed structure, making it possible to carry out screening tests and decreasing emotional distress [38]. The second group aims at selecting the best tests among the available ones [39; 40]. To do this, fuzzy logic, artificial intelligence (AI) and genetic algorithms are generally employed to reach a more significant joint interpretation of the scores of the typical dyslexia screening tasks [39] and to exclude the less predictive ones [40]. The third group is instead focused on jointly analyzing several dyslexia assessment tests, in order to build an automatic predictor of the presence/absence of the disorder. Again, AI perfectly suits this purpose, both for the large amount of data provided by the tests and considering the capability of machine learning (ML) algorithms to find significant relationships between the tested features and obtained results.