Medify
Built three AI systems for a medical-education platform that students use to revise against exam questions — a content audit engine, a knowledge-graph recommendation engine and a conversational study assistant.
Key Results
Content-audit accuracy across the testing dataset
Reduction in the content team's manual effort
AI systems delivered (audit, recommendation, assistant)
The Challenge
Medical students revise by working through thousands of exam-style questions, so the quality of that question corpus is everything. But as the dataset grew, overlaps, inaccuracies and gaps crept in — and finding them meant the content team manually reviewing huge volumes of questions, a slow and low-ROI task.
At the same time, every student was served broadly the same content, with no intelligent sense of what each individual should revise next.
The Solution
Acting as fractional Lead AI & Full-Stack Engineer, we designed and built three complementary AI systems, using Python, scikit-learn, TensorFlow, AWS and React.
AI Content-Audit Engine
Audited the question corpus to surface overlaps, inaccuracies and white space across the testing dataset with ~95% accuracy.
Knowledge-Graph Recommender
Turned each student's progress into a hyper-personalised, next-best learning path.
Conversational Study Assistant
Let students ask questions about the curriculum, individual test items and what to revise next.
The Impact
Tangible Outcomes
AI content audit reached ~95% accuracy across the testing dataset
Cut the content team's manual review effort by ~80%
Delivered a hyper-personalised, knowledge-graph learning-path engine
Launched a conversational AI study assistant for students
Key Takeaway
Embedded as the lead AI and full-stack engineer to take three distinct AI systems from concept to production, removing a major content bottleneck while making revision genuinely personalised.
Want to Put AI at the Heart of Your Learning Platform?
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