Every year, there is increasing interest in applying Artificial Intelligence (AI) algorithms and systems in education. Educating students about the conscious use of AI and its challenges is essential. Still, even before that, it is necessary to educate teachers who need to acquire the necessary skills to use these technologies in the classroom to enrich their students' learning experience. Training must be theoretical and guide teachers in designing educational activities with AI, about AI, and preparing for AI. This article presents research conducted in Italy to understand educators' attitudes toward AI in Education. Responses to a nationwide questionnaire are analysed to understand the relationship between teachers at all levels of schooling and AI. The results show that teachers need more confidence in their AI skills but are also not too concerned about the increasing spread of AI at various levels. From the findings, we can also say that AI has found little space in the school activities of Italian teachers. At the same time, teachers state that they urgently need to be trained on AI issues.

Research on Learning Analytics is closely related to the use of a Digital Learning Environment, which can be defined as a learning ecosystem in which to teach, learn and develop skills in the classroom, online or in hybrid mode. Using this, Educational Data, continuously updated and growing, have become Big Data. To make the most of these data, it is useful to use Learning Analytics techniques to analyze and interpret them, and to obtain enough information to make decisions. Predicting student learning outcomes is one of the main topics in learning analytics research. This research work has the main goal of building a model for predicting which score range students will achieve at the end of the online training. In this way, in future editions of the project, by analyzing the situation of students during the online training, we may provide students with personalized feedback to increase their involvement in the training and prevent dropouts. We analyzed three past editions of the Digital Math Training online training and developed two Random Forest models capable of predicting the final score range obtained by the students after the first three and the first six problems. We have also developed two Probabilistic Neural Network models with the same purpose, but with worse results.

This paper shows the model developed by the University of Turin to support students that must face the transition from the last year of secondary school to the first year of University. Integrations that are specifically designed for Learning Management Systems help sustain three effective actions conducted in synergy: increase students' awareness in the choice of the future course of study, support them in taking the admission tests and the first-year exams, allow the autonomous administration of admission tests led by the University. The methodological strategies adopted are presented and discussed based on the analysis of the data of the years 2015 and 2016.