AI in EdTech — Adaptive Learning Platform
Designed an AI-powered adaptive learning system that personalizes curriculum delivery, automates assessments, and measurably improves student outcomes.
Overview
Traditional EdTech platforms deliver the same content to every learner regardless of pace, proficiency, or learning style. I helped an EdTech company build an adaptive learning platform that uses AI agents to personalize the student journey — from content recommendations to automated grading and real-time intervention alerts for educators.
Problems We Solved
One-size-fits-all curriculum leading to high dropout rates and low engagement
Manual grading bottleneck — instructors spending 60%+ time on assessments
No early-warning system for at-risk students until it was too late
Fragmented content across video, text, and interactive modules with no unified progress tracking
Approach
- Learning profile engine
- Built a student proficiency model using assessment history, engagement signals, and time-on-task data to create dynamic learner profiles.
- Adaptive content sequencing
- AI agent selects the next learning module based on mastery level, learning style preference, and spaced-repetition scheduling.
- Automated assessment & feedback
- LLM-powered grading for open-ended responses with rubric adherence; instant feedback with explanations and hints.
- Educator dashboard & alerts
- Real-time dashboard showing class-wide and individual progress; automated alerts when students fall behind pace or show disengagement patterns.
- Content gap analysis
- Agent identifies topics where students consistently struggle, flagging content that needs revision or supplementary material.
Reference Architecture
- Orchestrator: Node/TypeScript with adaptive sequencing engine
- AI Layer: LLM with function calling for grading, content selection, and feedback generation
- Data Store: PostgreSQL for student profiles and progress; Redis for session state
- Content Delivery: Headless CMS with tagged modules (video, text, quiz, interactive)
- Analytics: Event-driven pipeline for learning analytics and intervention triggers
Outcomes
- 35% improvement in course completion rates within the first cohort
- 70% reduction in instructor grading time through automated assessment
- 28% faster concept mastery measured by pre/post assessment deltas
- Early intervention alerts caught 90% of at-risk students within the first two weeks
Key Metrics
| Metric | Before | After | Delta |
|---|---|---|---|
| Course completion rate | 52% | 70% | +18pp |
| Avg. grading time per assignment | 12 min | 3.5 min | −71% |
| Student satisfaction (NPS) | 34 | 61 | +27 |
| At-risk detection accuracy | Manual | 90% | New |
| Time to concept mastery | 14 days | 10 days | −28% |
Implementation Roadmap
- Week 1–2: Discovery — audit existing content, student data, and instructor workflows
- Week 3–5: Pilot — adaptive sequencing + automated grading for one course module
- Week 6–10: Scale — roll out across full curriculum with educator training and monitoring
Stack
- Node/TypeScript orchestrator, Next.js educator dashboard
- LLMs with function calling for grading and content selection
- PostgreSQL, Redis, event-driven analytics pipeline
- Headless CMS for content management
- OpenTelemetry for observability
Looking to bring AI-driven personalization to your learning platform? Let's start with a pilot on your highest-impact course.
