AI-Powered Adaptive Learning Platform
A UK-based EdTech provider with vocational and professional certification courses faced a 61% non-completion rate. Their static, video-heavy format failed to adapt to individual student pace and knowledge gaps, leading to poor learning outcomes and declining corporate ROI.
Built an "Adaptive Brain" for the learning platform that uses reinforcement learning to dynamically customize the content path for every student in real-time.
Engineered a pipeline to ingest PDFs and videos into a Neo4j graph, automatically identifying semantic relationships between learning modules.
Implemented an RL model that optimizes for long-term retention by selecting the "next best" content unit based on the learner's unique performance history.
Used fine-tuned LLMs to generate high-quality, varied quiz questions for every concept node, preventing "question bank fatigue."
Built a feature store to track thousands of micro-interactions (pause time, review frequency) to feed the adaptive and prediction models.
Reduction in non-completion from 61% to 34%
Improvement in average student performance vs. control
Corporate client retention rate post-deployment
Generated questions accepted by instructors without edits
Graph-based traversal that finds the optimal knowledge path for a student based on real-time comprehension signals.
Predictive modeling that identifies students likely to quit 7 days before their last session.
Maintaining the RL state across web and mobile, ensuring a seamless adaptive experience regardless of hardware.
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