Adaptive Course Pathways and Outreach Engine

A mid-sized education provider struggles to scale personalized advising: low conversion from inquiry to enrollment, unpredictable course dropouts, and advisors overwhelmed by manual outreach. This creates revenue leakage, poor student experience, and inconsistent guidance across programs.

Build a data-driven recommendation and engagement system that links student records, learning activity, and CRM signals to AI models, with human oversight and governance.

• Ingest and normalize SIS, LMS, CRM, application, and engagement data; apply privacy filters, consent checks, and role-based access.
• Train supervised models for intent and churn risk and a hybrid recommender (collaborative + content-based) to propose personalized course sequences and schedules.
• Use an LLM layer to generate plain-language explanations of recommendations, tailored outreach messages, and FAQ responses; attach confidence scores and citations to source materials.
• Surface recommendations and message drafts in an advisor dashboard for review and edits (human-in-the-loop), and orchestrate multichannel delivery (email, SMS, in-platform nudges).
• Deploy with A/B tests, continuous monitoring for accuracy and fairness, logging for audits, and regular retraining cadence tied to performance metrics.

Teams typically see higher engagement and more efficient advising: targeted cohorts can expect inquiry-to-enrollment conversion lifts in the ~5-12% range and retention improvements of ~3-8 percentage points. Advisors can cut manual outreach time by ~30-50%, lowering cost per enrolled student by roughly 8-20%; results vary with data quality, program mix, and rollout rigor.