exAImination

Elevate Your Learning: AI-Powered Test Generator for Text

Devpost, Frontend sample

Project Description

We recognized the challenge of revising covered chapters and devised a solution utilizing AI. Our project leverages OpenAI's API, fine-tuned to read chapter/document data and generate questions. Users can upload .txt files or input text directly, generating a set of questions tailored to their input. Upon answering, the system evaluates responses, providing a score to users.

How It Works

1. Input Data:

   • Users upload .txt files or input text directly.
   • The AI processes the data and generates a set of questions.

2. Question Evaluation:

   • Users answer generated questions.
   • Backend evaluates responses, considering similarity scores for descriptive questions.

3. Immediate Feedback:

   • Users receive immediate feedback and a score reflecting their understanding.

4. PDF Integration (Future Enhancement):

   • Utilizing "pdfjs-dist" module, the system can read PDFs.
   • Frontend sends PDF to Backend for processing and evaluation using the fine-tuned model.
   • Users can download questions and answer keys.

Challenges Faced

• Integration of Technologies:

  • Seamless integration of Python-based AI model with Node.js backend and Next.js frontend required careful consideration of data formats and communication protocols.

• Ensuring Question Quality:

  • Maintaining clear, unbiased, and grammatically correct questions demanded continuous evaluation and improvement of the AI model's outputs.

Achievements

• Innovative Technology Use:

  • Utilized Next.js, Node.js, and Python for a seamless user experience and efficient data handling.

• Automated Test Generation:

  • Automated test creation based on parameters and learning objectives, saving time for educators and ensuring consistent, quality tests.

• Educational Contribution:

  • Contributed to educational technology by providing educators with innovative tools for student learning and assessment.

• Immediate Feedback and Resources:

  • Offered immediate feedback to users and downloadable questions and answers for further study.

Lessons Learned

• AI Integration and Fine-tuning:

  • Practical application of AI in web applications and model fine-tuning using Python.

• Full-Stack Development:

  • Hands-on experience with Next.js, Node.js, and Python for full-stack development.

• Effective Collaboration:

  • Developed collaboration and teamwork skills, enhancing communication and problem-solving abilities.

• Continuous Learning:

  • Embraced continuous learning, refining skills, seeking feedback, and incorporating new knowledge for project improvement.

Future Enhancements

• PDF Reading:

  • Implementing PDF reading using "pdfjs-dist" for expanded data input options.
  • Enabling PDF processing and evaluation through the fine-tuned model.

• Downloadable Questions:

  • Enhancing the download feature to include questions and answer keys for teaching purposes.

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This project was created for [EduLearn Hackathon], organized in [October 2023].