Describe your project

AlgoMentor is an AI-powered Socratic teaching assistant designed to simplify learning Data Structures and Algorithms (DSA) for beginners. The project focuses on delivering personalized, interactive learning experiences using GenAI to guide users through key DSA concepts.

In-Scope:
Interactive Learning: AlgoMentor provides real-time, tailored feedback on DSA concepts, helping users understand algorithms through questions and hints.
Beginner-Friendly: The platform is specifically designed for those new to DSA, focusing on basic to intermediate-level questions.
Socratic Method: By asking targeted questions and guiding users step-by-step, the tool enhances understanding and critical thinking.
GenAI-Powered Assistance: AlgoMentor leverages AI to dynamically adjust question complexity based on the user's progress.

Out of Scope:
Advanced DSA: Complex topics beyond intermediate levels (e.g., advanced graph theory) are not included at this stage.
Real-Time Collaboration: Features like pair programming or real-time discussions with mentors are outside the current scope.
Non-DSA Topics: Subjects unrelated to algorithms and data structures, such as machine learning or systems design, are not part of the current offering.

Future Opportunities:
Advanced DSA Content: Expanding the platform to include more advanced algorithms and optimizations.
Personalized Learning Paths: Developing customized learning tracks based on user progress and performance.
Integration with Coding Platforms: Allowing users to practice coding directly on the platform with problem sets and challenges.
Community Engagement: Creating forums or study groups where users can discuss topics, share insights, and ask questions.

AlgoMentor provides a foundation for scalable, AI-driven learning, with future potential to expand into more areas of computer science education.

Challenges I ran into

During the development of AlgoMentor, I encountered a significant challenge in integrating the GenAI model with the user interface to provide seamless, real-time feedback. The objective was to ensure that users received dynamic responses based on their queries without compromising performance, particularly when dealing with complex data structures and algorithms.
To address this issue, I focused on optimizing the API calls to reduce latency. I implemented a caching mechanism to store frequently accessed data, minimizing redundant requests and enhancing the overall responsiveness of the application. This allowed the system to deliver prompt feedback while also reducing the load on the server.
Moreover, I conducted extensive testing to identify bottlenecks in the data processing workflow. By analyzing user interaction patterns, I was able to fine-tune the GenAI model to handle multiple queries simultaneously without sacrificing accuracy and will do same more rigorously in future updates. This iterative process involved adjusting the model's parameters and improving the underlying algorithms to ensure efficient handling of user requests.
Additionally, I prioritized user experience by designing an intuitive interface that guided users through their learning journey. The feedback loop was refined to provide constructive insights tailored to individual learning styles, making the interaction with the AI mentor not only informative but also engaging.
Ultimately, these strategies enabled me to overcome integration challenges and deliver a robust learning platform that meets the needs of beginners in data structures and algorithms, setting the stage for future enhancements and scalability.