Academic Guidance & Monitoring Intelligence System

Educational institutions collect academic data constantly — grades, attendance, assessments — but rarely convert it into actionable insights. The data lives in spreadsheets and disconnected systems, so risks go unnoticed until it’s too late.

Through deep dives into institutional workflows, it became clear that each role sees only a fragment: students see a report card, faculty see a class, and administrators see summaries. Without a shared intelligence layer, no one can act early.

A data pipeline ingests and cleans academic records into a consistent model. AI/ML components score risk and generate guidance. A presentation layer renders role-specific dashboards. The design favors clarity and maintainability.

• Data ingestion — automated extraction of records from ERP systems.
• Cleaning and validation — handling missing values and normalizing scales.
• Feature engineering — calculating rolling averages and momentum scores.

• Risk scoring — predicting likelihood of academic probation.
• Context-aware guidance — tailored recommendations for study habits.

• Predictive risk models for early dropout detection.
• Deeper personalization in the AI guidance engine.
• Parent and counselor views for a fuller support network.
• Integrations with existing LMS and ERP systems.
• Automated reporting for accreditation and reviews.