Learning strategies and AI: a new contribution to Educational Quality at high school

<< Background >>We applied for this project because the participating schools and organisations had identified a clear need to modernise their educational practices and strengthen their capacity to address increasingly diverse learning profiles. Before the project, teachers lacked structured tools to analyse students’ study habits and learning styles, and schools had limited digital systems to support data-informed teaching. Moreover, both Italian and Hungarian partners expressed the need to enhance digital readiness and adopt evidence-based methods for personalising instruction, in line with Erasmus+ priorities on innovation and digital transformation in school education.The project addressed these needs by introducing a structured, research-based questionnaire, by supporting teachers in understanding learning variability, and by implementing digital and AI-assisted tools to interpret educational data. Partners also sought to reinforce their capacity to integrate adaptive methods into classroom practice, improve students’ engagement, and promote more inclusive and tailored pedagogical approaches.<< Objectives >>The project aimed to strengthen the capacity of participating schools to adopt data-informed and personalised approaches to teaching and learning. More specifically, it sought to digitalise and administer a validated questionnaire on learning strategies in order to obtain a structured and comparable dataset across Italian and Hungarian schools. The project also aimed to develop an initial AI-assisted system capable of analysing students’ responses and generating individualised reports for each learner, offering teachers evidence-based insights into study habits, cognitive preferences and areas requiring support.A further objective was to enhance teachers’ digital and analytical competences, enabling them to interpret the reports and reflect on how learning variability affects classroom dynamics. The project also intended to reinforce cross-country collaboration on innovative educational practices and to provide schools with a model for integrating research-based tools into their internal quality processes. Overall, the project aimed to lay the groundwork for more personalised, inclusive and effective learning environments supported by digital and AI-driven methodologies.<< Implementation >>The project was implemented through a coordinated sequence of activities involving all partners. A management structure was established at the outset to organise responsibilities, communication routines and monitoring procedures. The consortium digitalised the validated learning-strategies questionnaire and prepared it for administration in the participating Italian and Hungarian schools.EduBase developed the digital platform used to deliver the questionnaire and display results. After internal testing, schools carried out the data collection phase, supported by instructions and technical assistance. The project team analysed the dataset and produced individual student reports generated through an AI-assisted interpretation of learning styles, study habits and cognitive preferences.Teachers received guidance on how to read and use these reports to better understand learning variability within their classes. Feedback was gathered from schools on the clarity, usefulness and applicability of the outputs. Dissemination activities were carried out through online communication, meetings with stakeholders and the sharing of materials. A final evaluation reviewed the coherence, quality and impact of all activities implemented.<< Results >>The project produced several concrete outputs. The learning-strategies questionnaire was fully digitalised and administered in all participating schools, generating a cross-country dataset on students’ study habits and learning preferences. An AI-assisted analysis system was developed to process the collected data and produce individual student reports. These reports provided structured insights into learning styles, cognitive tendencies and areas requiring support. Each school received a consolidated analytical report summarising key trends, differences among groups and implications for teaching practice. Teachers were provided with guidance on how to interpret the reports and use the information to better understand variability within their classes. The project also strengthened cooperation among partners and enhanced their digital and analytical competences. Dissemination outputs included presentations, internal documentation and online communication. Overall, the project delivered a functioning digital tool, a validated dataset, AI-generated individual profiles and school-level analyses that remain available for future use.