## 4 Conceptual Design of BESPECIAL
### First Development Stage
#### 4.1.4 AI Assessment Module  
The input coming from the questionnaire and the clinical reports will be passed to the AI assessment module, first to train it and then, after the training will be completed, to provide those pieces of information about BESPECIAL users that allow predicting the most suitable supporting material for each of them. To develop the module, ML techniques will be used. At this stage, indeed, it is not advisable to rely on deep learning (DL), since the input (namely, the issues experienced by dyslexic students) and the output (the supporting tools and strategies) variables are not a huge number. Using DL is, thus, likely to result in data overfitting. At the second stage, instead, when a much higher number of variables will have to be processed, the possibility to switch to DL techniques will be taken into account. The good practice of testing several ML algorithms will be followed. In particular, supervised algorithms will be considered, since the questionnaire and the clinical reports provide labeled output variables, for which the label is given by the supporting tools and strategies. The typical and state-of-the-art set of ML algorithms will be used. It comprises naive Bayes, logistic regression, k-nearest neighbors, random forest, gradient boosting tree, SVM and ANN [78]. After training them and build a predictor for each, cross-validation will be carried out, so as to choose the best performing one. Accuracy and precision metrics (like AUC, F-measure, etc. [79]) will be taken into account as performance criteria. To take account of the possible presence of highly variable data, the algorithms testing will be run on different database setups, namely, rearranging data in different ways. For example, a different ranking scales for the answers to the questionnaire could be adopted, like scores from 1 to 3 (obtained by opportunely aggregating the original scores from 1 to 5), or the questions could be considered as \"yes/no\" ones (by collapsing the original 1 to 5 scores into \"yes\" and keeping the \"no\" answer). Similarly, different ways to aggregate and rank the information gained from the clinical reports will be considered. Furthermore, protocols to manage possible missing data will be developed. This step is necessary for two reasons. The main one is that clinical reports are compiled by human beings and guidelines on how to do it correctly are still lacking. This results in documents that are often and considerably incomplete. The other reason is that the questionnaire features the option \"never tried\" and \"don't know\" among the answers about the most useful supporting methodologies. Obviously, these answers cannot be considered the same as \"not useful\" and, thus, they must be treated as missing data. All the typical strategies to address this issue will be applied, like simple deletion, imputation and prediction and the techniques that will give the best result will be implemented. Once the AI module will have made its prediction, the results will be split into two parts. The most suitable tools for the user will be passed to the digitalization module, whereas the most suitable strategies will be one of the final output of BESPECIAL platform, which will be provided to the institutions and the teachers to orient their interventions in favor of the students.