Product Strategy
System Design
AI Powered Personalized Learning Recommendations
An AI engine guiding learners to courses that match their goals and boost platform growth.
By: Itisha Dubey
Problem Statement & Objectives
Learners struggle with too many choices, leading to disengagement and churn. Our mission: transform a passive catalog into a proactive, intelligent learning guide.
+25%
User Engagement
Increase platform time via relevant discovery.
+30%
Completion Rate
Reduce dropouts with adaptive guidance.
+20%
Satisfaction Score
Deliver tailored, non-generic experiences.
Our Users
Serve four distinct user segments, each with unique needs.
Learners
Discover relevant paths, stay motivated, complete courses
Instructors & Content Creators
Reach audience, understand drop-offs, Spot gaps, optimize content discoverability
Platform Admins
Tune algorithms, monitor KPIs
Features Recommended: Learners
AI Learning Path Agent
Conversational onboarding to understand goals and skill gaps
  • Builds personalized multi-course learning paths
  • Continuously adapts as learner progresses
RAG-Powered Course Preview & Q&A
Ask specific questions before enrolling
  • Answers grounded in actual course content
  • Reduces enrollment mismatch
Adaptive Re-engagement Agent
Monitors dropout signals and inactivity
  • Sends contextual nudges (e.g., "60% complete, Module 5 is 18 mins")
  • Improves completion rates
Learning Style Detector
Observes interaction patterns (skips, rewinds, quiz performance)
  • Silently adjusts recommendation formats
  • Matches natural learning behavior
Features Recommended: Platform Admins
Recommendation Control Tower
  • Real-time KPI dashboard (CTR, enrollment, completion, satisfaction)
  • Manual intervention controls (boost/suppress courses, adjust weights)
  • No engineering ticket required
Cohort-Based Campaign Agent
  • Auto-identifies at-risk learner cohorts
  • Launches targeted campaigns (course bundles, nudges, live sessions)
  • Direct intervention before churn occurs
Features Recommended: Instructors & Creators
Content Gap & Demand Signal Dashboard
Analyzes learner search queries and forum threads
  • Integrates job market data
  • Shows exactly what content is missing and in-demand
AI Course Metadata & Quality Agent
Auto-generates skill tags and difficulty scores
  • Maps prerequisites from course content
  • Benchmarks quality against similar courses
Trend-Aware Content Brief Generator
Combines platform trends with industry signals
  • Generates ready-to-use content briefs
  • Includes module structure, target persona, and demand estimates
Non-Functional Requirements
Performance
  • Recommendations: <300ms response (95% of requests)
  • RAG Q&A: <2 seconds
Scalability
  • 10x peak traffic support
  • 50M+ events/day ingestion
Availability
  • 99.9% uptime (learner-facing)
  • 99.5% uptime (admin tools)
Compliance
  • GDPR & CCPA ready
  • EU AI Act transparency
Security & Data Retention
  • Encrypted at rest and in transit
  • Role-scoped access
  • Auto-purge after 24 months
Monitoring & Logging
  • Full recommendation context logged
  • Real-time drift alerting (15% threshold)
Extensibility
  • Modular architecture for new signals
  • Swappable LLM backends
Core System Architecture
Five modular layers enabling independent scalability and continuous learning.
Separation of recommendation engine from agent layer allows independent evolution of matching logic and proactive intelligence.
Trade-Off: Personalisation vs. Privacy
The Challenge:
Better recommendations require more granular behavioral data. But aggressive tracking erodes trust and violates GDPR, CCPA, and EU AI Act requirements.
Our Decision: Privacy-First Personalisation
01
Tiered Data Collection
  • Low-sensitivity signals (completions, quiz scores) on by default
  • Fine-grained signals (video scrub positions) opt-in only
02
Early Anonymization
  • Compute dropout probability scores before sensitive data hits warehouse
  • Agents work with aggregated signals, not raw behavioral trails
03
Visible Explainability
  • Show learners why courses are recommended
  • "Recommended because you completed X and target Y role"
  • Builds trust through transparency
Bottom line: Slightly lower precision for opted-out users. Long-term gain: a platform learners trust.
Summary
Key Takeaways
1
Content-first
Prioritizes learner trust, content quality, and privacy.
2
Proactive, not passive
Agent layer acts as an intelligent companion, guiding and adapting in real-time.
3
Modular and extensible
Each layer is independently scalable and swappable (LLMs, data sources), built for long-term growth.
4
KPI-driven decisions
Engagement, completion, and satisfaction validated every feature prioritization and trade-off.
Assumptions & Future Roadmap
Key Assumptions
Data
  • Platform has sufficient learner interaction data for collaborative filtering
  • External market signals accessible via APIs
Users
  • Learners complete onboarding with honest, detailed input
  • Creators act on demand signals
Technology
  • LLM backend remains reliable and cost-effective
  • Infrastructure supports real-time event streaming
Compliance
  • GDPR & CCPA primary frameworks
  • Future markets addressed as needed
Future Enhancements
  • Multimodal Learner Profiling: Skill verification through assessments and credentials
  • Social & Cohort Learning: Peer-based recommendations and study groups
  • Employer Integration: Verified credentials recognized by hiring partners
  • Real-Time Content Personalization: Adaptive in-course experience
  • Federated Learning: Privacy-preserving model training
  • Multilingual Expansion: Regional markets and languages