Predict and Prevent Teacher Turnover

School districts and higher-education HR teams face chronic staffing gaps as teachers and adjunct faculty leave with little early warning. Manual signals (absences, exit interviews) arrive too late, causing repeated rehiring, loss of institutional knowledge, and variability in student outcomes.

Build a small, governed analytics and workflow layer that combines explainable churn models with targeted recommendation generation and human-in-the-loop case management. Start with a pilot on a defined workforce cohort, validate fairness and accuracy, then operationalize alerts into HR workflows and manager coaching.

• Ingest structured data (HRIS, payroll, time/leave, performance reviews, classroom observation scores, LMS engagement) and unstructured text (exit surveys, teacher comments) with secure pipelines.
• Train an interpretable churn model (gradient boosted trees + SHAP, or logistic regression for transparency) to score risk and surface the strongest drivers for each individual.
• Use NLP to cluster and summarize free-text reasons; use an LLM to draft personalized retention actions and professional development plans for manager review.
• Route prioritized alerts into HR casework tools (ticketing, manager dashboards) with recommended next steps; require HR/manager sign-off before any outreach.
• Apply governance: fairness testing by demographic subgroup, consent and data minimization, access controls, logging for audits, and a defined review cadence to recalibrate models.

Illustrative impact: teams typically see a 15-30% relative reduction in turnover within the flagged at-risk group after deploying targeted interventions. Preventing a single teacher departure can save roughly $8k-$20k in recruitment, onboarding, and lost instructional continuity; a mid-sized district can often reach payback in 6-12 months. Non-financial benefits include faster vacancy fill times and small but measurable improvements in teacher engagement and classroom stability.