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.

In a problem-solving activity, generalizing is an important process by which the specifics of a solution are examined. Technologies support this process, making it possible to create interactive explorations that allow to see how the result changes as the initial data vary. In this article we focus on the generalization of the solution process during a mathematical problem-solving activity using an Advanced Computing Environment (ACE). Our research questions are: how can we analyze the skills students develop while generalizing a problem? What are the most frequent difficulties? We analyzed the solution of a problem-solving activity with an ACE submitted by 75 students using a model specially developed by us for studying generalization using interactive components. The model considers three phases: design and choice of interactive components, programming of the system and control stages of generalization of a problem. For each stage we established a set of indicators to understand the competences achieved by each student. The results show that the students generalized the problem using different strategies, with some difficulty in the programming and control phase. The model developed allows to reflect on the skills achieved by students in the various phases of the generalization process.

In teaching it has become increasingly important to use didactic approaches that see students active and protagonists of their own learning. These approaches can often be supported by technologies, which also enable students to acquire digital skills and provide them with immediate and interactive feedback. In this paper we present recent research activities characterized by Data Driven Learning methodologies within a Digital Learning Environment integrated with an automatic formative assessment system to propose activities on the specialized language of Mathematics. In fact, Mathematics has always been one of the school disciplines in which students of all grades encounter the greatest difficulties. Numerous studies in Mathematics education have shown that the causes of disciplinary learning difficulties are the acquisition, understanding and management of one's language for specific purposes. The research activity involved 4 classes of two Italian secondary schools for a total of 80 students of grade 11 and their teachers. In this paper we study the impact that this type of activity has had on students, analyzing the students' responses to the final satisfaction questionnaire.