StudentRisk AI – Predicting student dropout and wellbeing using AI and analytics (studentrisk.admnwizard.com)

A new AI initiative, StudentRisk AI, has been launched to predict student dropouts and assess overall student wellbeing through advanced analytics. The system currently analyzes data from 1,250 students, identifying 178 as high-risk for dropping out, with an alarming average dropout probability of 58% and an average attendance rate of just 72%. Utilizing various visual data representations, such as heatmaps and scatter plots, the platform provides educators with crucial insights into factors influencing student engagement, including stress levels, exam scores, and family income. This development is significant for the AI/ML community as it harnesses predictive analytics to address educational challenges, aiming to improve retention rates and student support. By prioritizing interventions based on dropout risk trends and other metrics, educators can tailor strategies to help at-risk students, creating a more effective learning environment. The implementation of such AI-driven tools not only enhances the educational landscape but also demonstrates the potential of machine learning in addressing societal issues.