Test AI Student Answer Evaluation

# Freelancer Required: AI-Based Student Answer Evaluation Algorithm Testing

## Project Overview

We are looking for a developer to experiment with and improve answer-evaluation algorithms for an interactive educational practice platform designed for CBSE Classes 1–12, with future expansion to higher education.

**The core platform has already been built.** This project focuses on testing different algorithms for evaluating student answers, comparing their accuracy, and identifying the most effective approach. We are not looking to build a new website from scratch.

The platform allows students to practise questions, submit answers, receive feedback, understand mistakes, and improve through repeated attempts.

## Project Objectives

The main goal is to determine how accurately different algorithms can evaluate student responses against expected answers and marking schemes.

The system should recognise that students can express the same concept using different words. It should also identify incomplete answers, missing concepts, factual errors and irrelevant responses.

For example, an answer may be conceptually correct but worded differently from the model answer. Simple text matching may mark it incorrect, whereas semantic evaluation may recognise its meaning.

## Scope of Work

### 1. Review the Existing Platform
- Inspect the existing codebase and answer-checking workflow.
- Understand the current architecture and evaluation logic.
- Identify how different algorithms can be integrated without disrupting existing functionality.
- Reuse existing components wherever possible.

### 2. Experiment with Answer-Evaluation Algorithms

Explore and, within the agreed scope, implement approaches such as:

- **Text matching:** Compare answers after normalising capitalisation, punctuation and whitespace.
- **Keyword-based evaluation:** Check for essential concepts, required terms and missing points.
- **Semantic similarity:** Use text embeddings or suitable NLP techniques to assess whether differently worded answers communicate similar meanings.
- **AI-based evaluation:** Use a language model to evaluate responses against a model answer or marking scheme, where practical.

The objective is to compare these approaches rather than simply integrate an AI API. The developer should recommend which methods are feasible within the available budget.

### 3. Evaluate Different Answer Types

The evaluation logic should account for different response categories:

- Correct and complete answers.
- Correct but incomplete answers.
- Partially correct answers.
- Incorrect or factually misleading answers.
- Irrelevant or blank responses.

Where questions carry marks, the system should consider essential answer points and partial-credit criteria rather than relying only on overall text similarity.

The initial experiment may focus on descriptive answers and a small number of representative question types. Mathematics, numerical questions, Accountancy tables and other structured responses can be considered where feasible.

### 4. Test with Sample Student Answers

Create or use a small test dataset containing questions, model answers, marking criteria and sample student responses.

Include examples with:
- Correct answers using different wording.
- Missing concepts or incomplete explanations.
- Incorrect answers containing some correct keywords.
- Common misconceptions and irrelevant responses.

Compare algorithm outputs against expected results or manually reviewed reference labels.

### 5. Compare Performance

Where practical, compare the approaches using:

- Accuracy against reference evaluations.
- Correct answers wrongly marked incorrect.
- Incorrect answers wrongly marked correct.
- Agreement with expected marks for partially correct answers.
- Consistency, processing time and API costs where relevant.

Provide a concise comparison explaining which methods perform best on the tested examples and where they fail.

### 6. Integrate and Document

Integrate the agreed algorithm or algorithms into the existing answer-checking workflow without unnecessarily changing the platform.

Provide:
- Working implementation of the agreed scope.
- Test results and a brief comparison report.
- Source-code changes in the agreed repository.
- Basic setup and testing instructions.
- Known limitations and recommendations for future improvements.

## Technical Requirements

- Work with the existing technology stack wherever possible.
- Keep evaluation logic modular and maintainable.
- Avoid unnecessary rewrites or unrelated features.
- Handle errors and unavailable services appropriately.
- Protect API keys and credentials.
- Disclose any third-party API dependencies and costs before implementation.

## Budget and Timeline

**Fixed budget: ₹2,000 INR.**

This is a small experimental assignment. The existing platform is already built, and the work is limited to testing and implementing a clearly agreed subset of evaluation approaches.

Please propose a realistic scope that fits this budget. Specify which algorithms you can test, how many sample answers you can evaluate, what integration is included, and your estimated delivery timeline.

Third-party API usage charges, if any, must be disclosed separately before work begins.

## Ideal Freelancer

Experience in one or more of the following is preferred:

- Full-stack development.
- Python, JavaScript or TypeScript.
- Natural language processing (NLP).
- Text embeddings and semantic similarity.
- AI/LLM integration.
- Automated testing and evaluation metrics.
- Educational technology or assessment systems.

Practical experience validating algorithm performance is more valuable than simply connecting an AI API.

## How to Apply

Please include:
1. Relevant experience or portfolio links.
2. Your recommended approach for comparing answer-evaluation algorithms.
3. What you can realistically deliver within ₹2,000.
4. Estimated timeline.
5. Any additional API or service costs.

## Definition of Success

Success means demonstrating, using a small but meaningful test dataset, how different evaluation approaches handle alternative wording, incomplete answers and incorrect responses.

The final outcome should help us understand which approach offers the best balance of accuracy, consistency, cost and usefulness for students.

**Important:** This is an algorithm-testing and proof-of-concept project on an existing platform—not a request to build an entire educational website from scratch.