TeachifyAI

Re-Inventing e-learning, the right way

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Inspiration

As virtual classrooms become increasingly prevalent, understanding and responding to students' emotions in real time is crucial for effective online education.

What it does

Our product, Teachify, revolutionizes online education by incorporating real-time emotion recognition technology into virtual classrooms. Through seamless integration with platforms like Google OAuth, it offers educators a dynamic interface to engage with students. Using audio, video, and text inputs, Teachify continuously analyzes student emotions, displaying them through emojis and graphs. It pinpoints moments of distraction and notifies teachers, empowering them to tailor their teaching strategies. With features like customizable study plans based on emotional analytics and engagement metrics, Teachify transforms education, one emotion at a time.

How we built it

  1. We all initially started with the backend
  2. we created a complete workflow for the entire web project
  3. Delegated tasks within a team where 1 person did the ML part, 2nd did the API creations & integrations with opencv monitoring & 3rd person did the whole frontend

Challenges we ran into

We encountered several challenges throughout the development process. Integrating multiple technology stacks, including Django (Python) for the backend and React/NexJS (JavaScript) for the front end, presented a steep learning curve, particularly since it was our first time using Django. Coordinating between different languages and frameworks added complexity to the project. Additionally, sourcing open-source models suitable for our needs and training them with our data proved to be a challenging task. Despite these hurdles, our team persevered, leveraging collaborative problem-solving and continuous learning to overcome obstacles and deliver a successful product.

Accomplishments that we're proud of

We're proud of achieving seamless integration of emotion recognition technology into virtual classrooms, revolutionizing e-learning. Our platform enables real-time analysis of student engagement, displayed through intuitive visualizations like emojis and graphs. Our custom-trained DeepFace model accurately predicts emotions, while Librosa aids in assessing voice clarity. Despite time constraints, Firebase ensures secure data management. This accomplishment marks a significant step in personalized online education, empowering educators with actionable insights

What we learned

In addition to the broader educational insights gained, we've also acquired technical expertise in integrating diverse technologies seamlessly. Integrating machine learning (ML) with Django involved developing APIs to interact with ML models, enabling real-time emotion recognition. Firebase integration provided a robust backend solution for data storage and authentication, ensuring security and scalability. Leveraging React for the frontend facilitated dynamic user interfaces, enhancing user experience. Managing different languages and frameworks within a unified ecosystem required careful coordination and effective communication among team members. This experience underscores the importance of interoperability and collaboration in complex software development projects.

What's next for TeachifyAI

1.Look forwards to establishing partnership amongst schools, coaching classes, seminar speakers, etc. We will provide our software on a commission basis.

  1. Try to make our ML models as headless as possible. Devolop a extension or a plugin to be run in browser which will get activated when someone uses lets say Gmeet.
  2. Fine-tuning our deep learning DeepFace model, for specific audience or demographic needs, such as an Indian audience, is a valuable effort to improve model performance and relevance.

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