Learning Intelligence Platform
A coordinated system of 6 specialized AI agents processes learner behavioral data, predicts dropout risk using ensemble ML models, analyzes content effectiveness through engagement patterns, segments learners for personalized pathways, generates AI-crafted intervention messages with optimal timing, and calculates business impact with ROI projections—all presented through an executive intelligence dashboard..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Platform Configuration - Data source setup and analysis parameters
AI Agents Processing - Real-time data ingestion and risk prediction
Intelligence Hub - At-risk learners and content effectiveness insights
Analytics Report - Executive summary with performance metrics and recommendations
AI Agents
Specialized autonomous agents working in coordination
Data Ingestion Agent
Learning data from multiple LMS sources arrives in varying formats with quality issues. Raw data cannot be directly analyzed without validation, transformation, and feature engineering.
Core Logic
Processes and validates incoming learner data through a multi-phase pipeline. Validates data schema and completeness, extracts 156 behavioral and performance features per learner (engagement velocity, content affinity scores, temporal patterns), and prepares normalized data for downstream agent processing.
Risk Prediction Agent
Identifying at-risk learners manually is reactive—problems are discovered after dropout occurs. Organizations need early warning signals to intervene before learners disengage completely.
Core Logic
Uses ensemble ML models to predict learner dropout risk with 87% AUC-ROC accuracy. Engineers risk-predictive features, runs model inference to classify learners by risk level (critical/high/medium/low), calibrates probability scores, and generates SHAP explanations that identify specific factors contributing to each learner's risk assessment.
Content Analysis Agent
Learning content creators lack objective data on which modules effectively teach concepts and which cause learner frustration, abandonment, or poor knowledge retention.
Core Logic
Analyzes content effectiveness through learner engagement patterns. Calculates effectiveness, engagement, and retention scores per module, identifies drop-off points with timestamps and affected learner counts, detects content issues (complexity, length, accessibility), and generates prioritized recommendations for content improvements with expected impact projections.
Personalization Agent
One-size-fits-all learning paths fail to account for different learning speeds, preferences, and prior knowledge. Fast learners become bored while struggling learners become overwhelmed.
Core Logic
Segments learners into behavioral clusters using clustering algorithms (Fast Learners, Visual Learners, Hands-on Learners, Struggling Learners). Designs optimal pathways per segment with adaptive rules that trigger content adjustments based on learner behavior (e.g., skip remediation for high scorers, offer alternative formats for repeated content struggles).
Intervention Agent
Generic intervention messages have low response rates. Timing outreach incorrectly (wrong day, wrong time) further reduces effectiveness. Manual personalization at scale is impossible.
Core Logic
Generates personalized intervention strategies with AI-crafted messages. Prioritizes learners by intervention ROI, selects optimal strategies (outreach, content recommendations, mentor assignment), generates messages with personalized fields and appropriate tone, and optimizes send timing based on historical response patterns (e.g., 10 AM Tuesday sends show 2.3x higher response).
ROI Intelligence Agent
Learning leaders struggle to demonstrate business value of training investments. Without clear ROI data, training budgets face scrutiny and strategic decisions lack financial justification.
Core Logic
Calculates business impact by correlating training to outcomes using causal attribution methods. Computes program-level ROI from completion improvements, incident reductions, time-to-competency gains, and retention effects. Projects future business value, identifies high-ROI investment opportunities, and generates executive-ready financial analyses with confidence intervals.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
6 technologies
Architecture Diagram
System flow visualization