NavShikshakAI

Personalizing education with AI tutors that understand you.

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Inspiration

As a student, I have personally experienced the challenges of traditional teaching methods that fail to engage students in the joy of learning. Coming from a a country like India, where conventional teaching approaches are still prevalent, I found myself struggling to maintain enthusiasm for academics. However, I discovered that learning happens best when you are able to connect the new things you learn with something you already know, or when it's related to something you are interested in. This realization sparked the idea of NavShikshakAI, a platform designed to revolutionize education by providing personalized AI tutors that understand and cater to each student's unique interests and learning styles.

What it does

  • NavShikshakAI aims to transform the learning experience by making it more engaging and captivating for students. Through personalized AI tutors that comprehend each student's interests, the platform guides them through even the most complex topics using analogies and examples that lie in their interests. This approach not only enhances the students' understanding but also fosters a genuine enthusiasm for learning.

  • Additionally, the platform incorporates advanced problem-solving techniques, such as Chain of Thought (CoT), which breaks down complex questions into smaller steps. The AI tutor then guides students through each step, fostering a deeper understanding of problem-solving approaches and enhancing their critical thinking skills.

How we built it

The project leverages cutting-edge technologies, including:

  • NextJS for the backend
  • ReactJS for the frontend
  • FireBase for the database
  • Langchain for the AI capabilities

At the core, a dedicated Language Model (LLM) generates tailored prompts based on each student's interests. These prompts are then utilized by the primary LLM to provide personalized responses and explanations, ensuring a seamless and engaging learning experience.

Challenges we ran into

  1. Time Constraints: Despite the month-long hackathon, managing time effectively alongside academic commitments was a significant challenge, limiting the available time to work on the project until the final week.

  2. LLM Setup: Ensuring that the platform remains cost-free for all users was a priority. This presented difficulties in fine-tuning a custom LLM or hosting a proprietary model, as these options can be resource-intensive and expensive. To overcome this challenge, the project utilizes the Llama3 LLM and employs complex architectures during inference to provide a high-quality, personalized tutoring experience without incurring additional costs.

Accomplishments that we're proud of

  1. One of the most significant accomplishments of NavShikshakAI is its commitment to remaining completely free for users. By ensuring a free tier, the platform aims to reach and positively impact even the most underprivileged sections of society, providing them with access to quality education and opportunities for personal growth.

  2. We are also proud of the personalization with AI that we have achieved, making full use of AI to genuinely provide a tailored learning experience for each student, enhancing their lives and making a positive change in education.

What we learned

Throughout the development of NavShikshakAI, valuable lessons were learned, including:

  • Deploying complex LLMs cost-effectively while maintaining innovation
  • Effective time management and prioritizing the development of a Minimum Viable Product (MVP)
  • Implementing advanced AI techniques, such as CoT, for enhanced problem-solving capabilities

What's next for NavShikshakAI

The future roadmap for NavShikshakAI includes:

  1. Scaling and Outreach: Expanding the platform's reach to the communities and audiences that can benefit the most from personalized AI tutoring.
  2. Incorporating Feedback and Improvements: Continuously listening to user feedback and implementing enhancements to provide an even more tailored and effective learning experience.
  3. Regional Language Support: Introducing support for regional and local languages to cater to diverse linguistic backgrounds and make the platform more accessible.
  4. Advanced Model Development: Exploring the development of fine-tuned models that go beyond traditional LLMs, offering more refined and advanced problem-solving capabilities to support students in tackling increasingly complex questions.
  5. Automatic Timetable Generation: Implementing an automatic timetable generator that understands each student's unique needs, commitments, and learning preferences to create an optimal, personalized schedule, helping them manage their time effectively.

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