Artificial Intelligence (AI) is transforming education by enhancing adaptive learning through data-driven insights. This case study explores how AI can address challenges in education, such as school dropout rates, student collaboration, and skill development. The project, Data2Learn@Edu, aims to develop an AI-based dashboard to support teachers and decision-makers in identifying vulnerable students and adapting learning processes. The study focuses on learning analytics, which involves analyzing student data to improve educational outcomes. The project involves multiple institutions, including Politecnico di Torino, INVALSI, and the LINKS Foundation. The research uses clustering algorithms and data mining techniques to analyze student performance data and generate insights for educational decision-making. The findings highlight the potential of AI to personalize learning experiences and improve educational quality. The study also emphasizes the importance of ethical considerations, data privacy, and the role of human educators in complementing AI systems. The project's results demonstrate the effectiveness of AI in enhancing learning processes and supporting educational institutions in making data-informed decisions. The case study provides a framework for scalable, data-driven adaptive learning in education.Artificial Intelligence (AI) is transforming education by enhancing adaptive learning through data-driven insights. This case study explores how AI can address challenges in education, such as school dropout rates, student collaboration, and skill development. The project, Data2Learn@Edu, aims to develop an AI-based dashboard to support teachers and decision-makers in identifying vulnerable students and adapting learning processes. The study focuses on learning analytics, which involves analyzing student data to improve educational outcomes. The project involves multiple institutions, including Politecnico di Torino, INVALSI, and the LINKS Foundation. The research uses clustering algorithms and data mining techniques to analyze student performance data and generate insights for educational decision-making. The findings highlight the potential of AI to personalize learning experiences and improve educational quality. The study also emphasizes the importance of ethical considerations, data privacy, and the role of human educators in complementing AI systems. The project's results demonstrate the effectiveness of AI in enhancing learning processes and supporting educational institutions in making data-informed decisions. The case study provides a framework for scalable, data-driven adaptive learning in education.