AI Student Engagement System

A large public school district struggled to identify at-risk students early enough for meaningful intervention. With student data scattered across SIS, LMS, behavior tracking, and attendance systems, educators had no unified view of student health. The district partnered with Vitosha to build an AI-driven early warning and engagement system on the Microsoft Cloud, unifying data and automating outreach to keep students on track.

The district served 50,000+ students across 80 schools, with student data fragmented across four major systems that did not communicate. Educators spent hours each week manually reviewing reports to identify struggling students, often spotting issues too late. Only 12% of at-risk students received timely intervention, and dropout rates in the first year of high school were climbing. The district needed a solution that could unify data, predict risk early, and automate advisor outreach without adding staff.

Phase 1: Data Unification — Integrated SIS, LMS, behavior, and attendance data into Azure Data Lake and Dataverse, creating a single student 360 view with proper FERPA and GDPR controls in place. Phase 2: Predictive Model — Built Azure ML models to predict disengagement, course failure, and dropout risk using attendance patterns, grade trends, LMS logins, and behavior flags, generating weekly risk scores for all 50,000+ students. Phase 3: Advisor Workflow Integration — Connected risk signals to D365 Customer Service to auto-create cases for at-risk students and trigger advisor outreach via email, SMS, and the district app, reducing manual triage and ensuring no student fell through the cracks.

Early intervention accuracy improved by 35%, student engagement in platform activities rose 22%, and the system identified 89% of at-risk students at least six weeks before traditional indicators. Advisor productivity improved by 40% as automated case creation and outreach reduced manual triage by over 60%. The district reduced first-year dropout rates by 15% and scaled the platform from an initial 10-school pilot to all 80 schools within two semesters.

By unifying fragmented data and applying predictive AI, the district transformed its approach to student support. Early identification and automated intervention helped more students stay engaged and on track, demonstrating how a connected data platform can deliver measurable improvements in educational outcomes.