CodeAssist is an AI-powered learning ecosystem that dynamically curates problem sets, blogs, and challenges based on real-time user performance analysis.
- Dynamically selects problem sets, blogs, and challenges based on real-time user performance.
- Adapts to individual learning styles and capabilities.
- Assesses cognitive abilities like:
- Problem-solving speed
- Logical reasoning
- Memory retention
- Multitasking efficiency
If users get stuck, Echo suggests:
- Problem Tags
- Similar Problems
- Relevant Blogs
How It Works:
- Text vectorization is performed on 2500+ problems from various platforms.
- Cosine similarity is used to find the closest vector to the input query.
- Echo provides context-aware recommendations to help users solve problems efficiently.
- Adapts problem scheduling to a user’s biological clock:
- Mornings: Memory-intensive tasks.
- Evenings: Creative problem-solving tasks.
- Provides users with behavioral insights, including:
- Completion rates
- Accuracy levels
- Average session time
- Participation trends
- Focus levels & problem-solving habits
- Cognitive fatigue tracking to optimize learning breaks.
- Advanced behavioral analytics for deeper insights.
This project is licensed under the MIT License - see the LICENSE file for details.
Contributions are welcome! Feel free to open an issue or submit a pull request.
For any inquiries, reach out at: aryansatija2003@gmail.com