This project implements a simple AI-powered learning system that can explain educational topics, ask questions, evaluate student answers, and provide feedback.
The goal of this system is to simulate a basic AI tutor that helps students understand concepts interactively.
This project was developed as part of an AI Engineer Internship Assignment.
- Generate a simple explanation for a topic
- Ask questions to test student understanding
- Accept student answers
- Evaluate responses
- Provide score and feedback
- Python
- Groq LLM API
- Prompt Engineering
- CLI (Command Line Interface)
User Input → AI Explanation → Questions → Student Answers → Evaluation → Feedback
ai-learning-system
│
├── main.py
├── prompts.py
├── evaluator.py
├── requirements.txt
├── README.md
└── sample_io.md
Clone the repository
git clone https://github.com/yourusername/ai-learning-system.git
cd ai-learning-system
Create virtual environment
python3 -m venv venv
source venv/bin/activate
Install dependencies
python3 -m pip install groq
Set API Key
export GROQ_API_KEY="your_api_key_here"
Run the program
python3 main.py
- User enters class level, subject, and topic.
- The system sends a prompt to the LLM.
- The AI generates a simplified explanation.
- The system asks questions related to the topic.
- The student answers the questions.
- The program evaluates responses and provides feedback.
- Adaptive difficulty based on student performance
- Memory system to track student learning progress
- Reduce hallucinations with structured prompts
- Cost optimization using smaller models
If 10,000 students use this system daily, possible issues include:
- API rate limits
- High inference cost
- Increased latency
Possible solutions:
- Cache explanations for common topics
- Use request queues for processing
- Deploy scalable backend infrastructure
- Use smaller models for frequent queries
Darshan S BE Computer Science & Data Science PES College of Engineering