AI-Driven Student Assessment Reports
Budget: ₹12,500 – ₹37,500 INR
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.
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.