Case Study
An Educational Services Partner – AI-Powered Examination Evaluation
An educational services partner needed to automate the evaluation of practice exams for coaching institutes.
- Manual evaluation was slow, expensive, and could not scale to meet demand.
- Human graders introduced subjectivity and inconsistency into the scoring process.
- Ensuring factual accuracy with recent events and handling varied handwriting were difficult.
Solution Implementation
- An LLM-based OCR system was used to accurately digitize handwritten scripts.
- A RAG pipeline connected to a real-time news crawler ensured factual accuracy.
- A sophisticated framework used semantic clustering to ensure consistent, high-quality grading.
Technology Used
- Category : Technology/Platform/Methodology
- Large Language Models : Gemini Flash 2.0 (OCR), Gemini 2.5 Pro (Evaluation)
- Architectural Patterns : Retrieval-Augmented Generation (RAG)
- NLP & Embeddings : Transformer-based Sentence Embeddings (RoBERTa-like)
- Algorithms : Clustering (DBSCAN)
- Evaluation Frameworks : Chain-of-Thought Prompting, G-Eval (Meta-Evaluation)
- Data Infrastructure : Custom News Crawler, Vector Database
Results and Impact
- Automation massively improved the scalability and efficiency of the evaluation process.
- The system enhanced fairness by providing consistent, standardized grading.
- Students received higher quality, factually accurate, and detailed feedback.
- The service strengthened the credibility of the client's coaching programs.