Ticket Contents
Description
TAP currently uses advanced proprietary AI models (e.g., Gemini) to evaluate student-submitted artifacts such as drawings, prototypes, and written responses against rubric-based frameworks measuring 21st-century skills like creativity, critical thinking, problem-solving, and agency. While effective, the cost per evaluation is high and becomes prohibitive at the scale of millions of government school students. This project aims to fine-tune an open-source Vision Language Model (e.g., LLaMA or similar) to replicate this evaluation capability at significantly lower cost, targeting below ₹0.10 per assessment.
Goals & Mid-Point Milestone
Goals
Setup/Installation
No response
Expected Outcome
A fine-tuned open-source model capable of evaluating student artifacts
Cost-efficient inference pipeline
Evaluation benchmarks against existing systems
Documentation of training and deployment pipeline
Acceptance Criteria
No response
Implementation Details
- Dataset: TAP's existing collection of student artifacts (images/videos) paired with rubric-based evaluations
- Model: Open-source Vision Language Models such as LLaMA or similar architectures
- Frameworks: PyTorch / TensorFlow for training and fine-tuning
- Fine-tuning approach: Supervised fine-tuning optimized for structured rubric-based scoring
- Cost optimization techniques: model quantization, efficient inference architecture
- Benchmarking pipeline comparing model outputs with human evaluators and Gemini
Mockups/Wireframes
No response
Product Name
Open-Source AI Model for 21st Century Skills Assessment
Organisation Name
The Apprentice Project
Domain
Education
Tech Skills Needed
Artificial Intelligence, Machine Learning, Computer Vision, Natural Language Processing, Python
Mentor(s)
TBD
Category
Machine Learning
Ticket Contents
Description
TAP currently uses advanced proprietary AI models (e.g., Gemini) to evaluate student-submitted artifacts such as drawings, prototypes, and written responses against rubric-based frameworks measuring 21st-century skills like creativity, critical thinking, problem-solving, and agency. While effective, the cost per evaluation is high and becomes prohibitive at the scale of millions of government school students. This project aims to fine-tune an open-source Vision Language Model (e.g., LLaMA or similar) to replicate this evaluation capability at significantly lower cost, targeting below ₹0.10 per assessment.
Goals & Mid-Point Milestone
Goals
Setup/Installation
No response
Expected Outcome
A fine-tuned open-source model capable of evaluating student artifacts
Cost-efficient inference pipeline
Evaluation benchmarks against existing systems
Documentation of training and deployment pipeline
Acceptance Criteria
No response
Implementation Details
Mockups/Wireframes
No response
Product Name
Open-Source AI Model for 21st Century Skills Assessment
Organisation Name
The Apprentice Project
Domain
Education
Tech Skills Needed
Artificial Intelligence, Machine Learning, Computer Vision, Natural Language Processing, Python
Mentor(s)
TBD
Category
Machine Learning