Researchers have unveiled a predictive analytics platform that uses random forest models and explainable AI to identify college students showing early signs of academic stress and potential dropout. The system analyzes enrollment data, grades, attendance, and engagement metrics, delivering risk scores to advisors via a Flask dashboard.
By surfacing actionable insights, institutions can intervene with counseling, tutoring, or financial aid, improving retention rates. The study highlights the growing role of AI in higher education administration and raises questions about data privacy and the ethical use of predictive student profiling.
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