Our EdTech platform needs an AI module that automatically turns raw test-score data into clear, personalized assessment reports students and teachers can act on. I want an end-to-end solution: from ingesting a CSV or live feed of test results, through model-based analysis, to pushing a finished PDF/JSON report back to our LMS via a RESTful API. Core requirements • Input data: test scores only (for now). The design should leave room to add class-participation or assignment-grade signals later without a full rewrite. • Analytics: detect strengths, skill gaps, and mastery trends; flag anomalous scores; recommend next-step resources. Please choose the ML approach you feel suits the problem—traditional regression or modern transformer models are both acceptable as long as they’re explainable. • Output: individualized narrative feedback plus visualisations (progress graphs, competency heatmaps). • API: secure, token-based endpoint that accepts student IDs, returns report objects, and logs calls for auditing. Swagger or similar auto-generated docs are expected. • Turnaround: deliver an MVP ready for internal testing ASAP, followed by quick iterations until we sign-off. Acceptance criteria 1. Given a sample dataset of 500 students’ test scores, the system must generate reports with <5 sec average latency. 2. At least 90 % of teacher survey responses must rate the auto-generated feedback as “useful” or better. 3. The API must pass our security and load tests (2 k RPS sustained). If you have experience deploying ML in education and can move quickly, I’m ready to provide data samples and direct sandbox access as soon as we kick off.