Protégé

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  Home

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  Teach lessons to our AI agent to gain a deeper understanding of any subject through reflection.

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  Create lesson plans to customize your learning experience.

Inspiration

We were inspired by the protégé effect, a psychological phenomenon where teaching others helps reinforce the student's own understanding. This concept motivated us to create a platform where users can actively learn by teaching an AI model, helping them deepen their comprehension through explanation and reflection. We wanted to develop a tool that not only allows users to absorb information but also empowers them to explain and teach back, simulating a learning loop that enhances retention and understanding.

What it does

Protégé enables users to:

• Create lessons on any subject, either from their own study notes or with AI-generated information.
• Teach the AI by explaining concepts aloud, using real-time speech-to-text conversion.
• The AI then evaluates the user’s explanation, identifies errors or areas for improvement, and provides constructive feedback. This helps users better understand the material while reinforcing their knowledge through active participation.
• The system adapts to user performance, offering customized feedback and lesson suggestions based on their strengths and weaknesses.

How we built it

Protégé was built using the Reflex framework to manage the front-end and user interface, ensuring a smooth, interactive experience. For the back-end, we integrated Google Gemini to generate lessons and evaluate user responses. To handle real-time speech-to-text conversion, we utilized Deepgram, a highly accurate speech recognition API, allowing users to speak directly to the AI for their explanations. By connecting these technologies through state management, we ensured seamless communication between the user interface and the AI models.

Challenges we ran into

One of the main challenges was ensuring seamless integration between the AI model and the front-end so that lessons and feedback could be delivered in real time. Any lag would have disrupted the user experience, so we optimized the system to handle data flow efficiently. Another challenge was real-time speech-to-text accuracy. We needed a solution that could handle diverse speech patterns and accents, which led us to Deepgram for its ability to provide fast and accurate transcriptions even in complex environments.

Accomplishments that we're proud of

We’re particularly proud of successfully creating a platform that allows for real-time interaction between users and the AI, providing a smooth and intuitive learning experience. The integration of Deepgram for speech recognition significantly enhanced the teaching feature, enabling users to explain concepts verbally and receive immediate feedback. Additionally, our ability to simulate the protégé effect—where users reinforce their understanding by teaching—marks a key accomplishment in the design of this tool.

What we learned

Throughout this project, we learned the importance of real-time system optimization, particularly when integrating AI models with front-end interfaces. We also gained valuable experience in balancing accuracy with performance, ensuring that both lesson generation and speech recognition worked seamlessly without compromising user experience. Additionally, building a system that adapts to users’ teaching performance taught us how crucial customization and feedback are in creating effective educational tools.

What's next for Protégé

Our next steps include:

• Developing personalized lesson plans that adapt based on user performance in teaching mode, making learning paths more tailored and effective.
• Adding gamified progress tracking, where users can earn achievements and track their improvement over time, keeping them motivated.
• Introducing community and peer learning features, allowing users to collaborate and share their teaching experiences with others.
• Building a mobile version of Protégé to make the platform more accessible for learning on the go.

Built With

• deepgram-api
• google-gemini-api
• python
• reflex