Enterprise AI-Powered At-Risk Student Identification & Personalized Intervention System

Core Logic

The Orchestrator Agent manages the complete workflow pipeline implementing phased execution (data ingestion → risk analysis → diagnostic assessment → intervention planning → personalization → report generation). It dispatches tasks to specialist agents, aggregates results across pipeline stages, handles errors with graceful degradation, maintains session state and execution traces, and provides real-time progress updates through WebSocket streaming. Uses Claude Sonnet for efficient coordination with low latency.

Core Logic

The Data Retriever Agent connects to configured data sources via API integrations, fetches student records, course enrollments, assessment results, and engagement logs, validates data against expected schemas with error and warning classification, computes data completeness scores, transforms heterogeneous formats into unified data models, and tracks data freshness for staleness alerts. Uses Claude Haiku for fast, efficient data processing with schema validation tools.

Core Logic

The Risk Analyzer Agent generates risk predictions for each student using ensemble ML models trained on historical outcome data. It computes risk scores (0-100) with classification into low/medium/high/critical levels, provides confidence intervals using calibrated uncertainty estimation, generates SHAP-based feature importance explanations showing which factors increase or decrease risk, analyzes temporal trends to detect improving or declining trajectories, performs cohort comparison showing student percentile ranking, and includes model metadata (version, validation metrics, calibration scores) for transparency. Uses Claude Sonnet for reasoning about complex risk patterns.

Core Logic

The Diagnostic Agent performs comprehensive diagnostic analysis including Bayesian knowledge tracing to estimate mastery of each learning objective, gap identification comparing current mastery to target levels with priority ranking, error pattern detection analyzing incorrect responses to identify systematic misconceptions, strength identification to leverage existing competencies for learning transfer, root cause analysis classifying primary causes (knowledge gap, prerequisite missing, procedural error, conceptual misunderstanding, attention, time management, motivation, external factors), and learning style assessment for personalization. Produces knowledge graphs showing objective dependencies and blocked prerequisites.

Core Logic

The Intervention Planner Agent designs personalized intervention plans by selecting from intervention library (content modules, practice sets, tutoring, peer groups, office hours, adaptive quizzes, video lessons, projects, mentorship), matching interventions to diagnosed learning gaps and root causes, estimating individual treatment effects using causal inference methods with Cohen's d effect sizes and number-needed-to-treat calculations, designing timelines with milestones and checkpoints, adapting delivery method to learning style preferences (self-paced, scheduled, just-in-time, adaptive), and incorporating evidence base quality (RCT, quasi-experimental, observational) into recommendations. Supports A/B test assignment for intervention effectiveness research.

Core Logic

The Personalization Engine Agent tailors all intervention content and communications to individual learners. It adapts content format to preferred modalities (visual, auditory, reading, kinesthetic), adjusts difficulty and pacing based on demonstrated competency, personalizes communication tone (formal, friendly, encouraging) and channel (email, SMS, in-app, LMS), optimizes engagement timing based on historical access patterns, and creates personalized learning pathways with adaptive sequencing. Uses Claude Sonnet with higher temperature for creative content generation.

Core Logic

The Progress Monitor Agent tracks student progress continuously, computing engagement metrics (login frequency, session duration, resource access, completion rates), performance metrics (grade changes, mastery improvement, assessment scores, predicted final grade), milestone progress with blocker identification, adaptation triggers detecting when to adjust intervention plans, and alert generation for engagement drops, performance declines, milestone risks, and intervention non-access. Evaluates intervention effectiveness and triggers re-planning when goals are not being met. Uses Claude Haiku for fast, efficient monitoring with low latency.

Core Logic

The Report Generator Agent synthesizes all program data into comprehensive reports including executive summaries with key metrics and trend directions, student outcome distributions (recovered, in-progress, not-recovered), intervention effectiveness analysis by type with success rates and cost-per-student, ROI calculations (investment breakdown, returns from retention, instructor time savings, NPV, payback period), cohort analysis with heterogeneous treatment effects across subgroups, comparative metrics against baseline, previous period, and industry benchmarks, fairness metrics ensuring equitable outcomes across demographics, system health monitoring (uptime, response time, model drift), and strategic recommendations prioritized by impact and effort. Generates success stories documenting recovery journeys for institutional learning.