Politecnico di Milano and AI to reduce University dropout rates
How data analysis enhances the academic experience
Source: Il Sole 24 ore
Date: 16/04/25
Date: 16/04/25
Tackling university dropout is one of the most urgent challenges in building a more equitable and inclusive society. The Politecnico di Milano has shown that technology can become an ally of education, applying statistical models and artificial intelligence algorithms to identify at-risk students early on. The results are clear: in just two years, the dropout rate dropped from 20% to 10%, a 50% reduction.
An innovative project
The project was based on the analysis of a massive volume of data: over 110,000 students and 10 million entries, collected between 2010 and 2019. Each entry represents an event in the academic journey—enrollments, exams, results, course changes. The analysis focused particularly on academic paths completed between the 2010/2011 and 2015/2016 academic years in Engineering degree programs: 31,071 student records, of which 62.7% resulted in graduation, 21.7% ended in early dropout, and 15.6% in late dropout.
Through machine learning algorithms, the university was able to anticipate risk signals, enabling tailored interventions such as peer tutoring, psychological support, guidance tools, and scholarships. The results highlighted key indicators—for example, students who do not earn significant credits in the first semester are much more likely to drop out.
Furthermore, financial support such as that provided by DSU scholarships has shown a positive impact on academic continuity, especially for students from disadvantaged backgrounds. Active engagement through tutoring was also emphasized: participants receive a virtual badge that strengthens their sense of belonging to the university community.
Through machine learning algorithms, the university was able to anticipate risk signals, enabling tailored interventions such as peer tutoring, psychological support, guidance tools, and scholarships. The results highlighted key indicators—for example, students who do not earn significant credits in the first semester are much more likely to drop out.
Furthermore, financial support such as that provided by DSU scholarships has shown a positive impact on academic continuity, especially for students from disadvantaged backgrounds. Active engagement through tutoring was also emphasized: participants receive a virtual badge that strengthens their sense of belonging to the university community.
Regg3's Role: measuring, valuing, and supporting education
In Regg3’s model, education is a strategic lever for generating lasting social value. By analyzing data on access and educational quality, we help companies and institutions identify where and how to invest in order to strengthen communities.
The case of the Politecnico di Milano demonstrates that targeted, data-driven strategies can transform the education system into a more inclusive, resilient, and efficient space.
Regg3 makes these impacts visible and measurable—because building the future means investing in people.
The case of the Politecnico di Milano demonstrates that targeted, data-driven strategies can transform the education system into a more inclusive, resilient, and efficient space.
Regg3 makes these impacts visible and measurable—because building the future means investing in people.