Personal Research Project
Education / EdTech · 2026
Quetzal: AI-Native Learning Platform with Knowledge Extraction
A personal project: an AI-powered learning platform built around document-grounded tutoring, adaptive assessments, and multi-model knowledge extraction.
Context
Starting point and environment
Traditional learning platforms treat AI as a bolt-on feature. Students need systems that deeply understand their study material and adapt to their knowledge gaps.
Challenge
Core constraint to solve
Build a multi-stage knowledge extraction engine that decomposes academic documents into atomic, scorable knowledge units — with accurate difficulty calibration, deduplication, and coverage guarantees across diverse academic domains.
Solution
Design and implementation approach
Architected a constrained multi-model orchestration system (OpenAI, Gemini, Anthropic) with supervisor-based validation, anti-hallucination safeguards, and Bloom's Taxonomy-grounded difficulty calibration. Integrated OCR-based grading, progressive hinting, and Socratic interaction modes.
Outcome
Measurable impact and delivery result
A working platform that demonstrates multi-model AI orchestration at a level I'm genuinely happy with. Bloom's Taxonomy integration solved a calibration problem I hadn't expected to solve cleanly.
Key Takeaway
“No single model wins every task, so routing by strength gave better results. Adding strict validation cut hallucinations sharply, and Bloom's Taxonomy made generated learning content usable.”
