Enable students to grasp and understand Education Content better by customizing the learning path with the power of AI

Vision

Our vision is to enable the next generation of bright minds with a deeper understanding of the world and help them to create meaningful impact.

As a student, the main struggle in learning from large class sizes is grasping the content the way it was intended to be understood. Each individual student has their own way of thinking and learning. They also learn at different speeds and need different levels of attention. For example some students grasp math much faster than the other kids and usually spend the rest of the class hours being distracted. Meanwhile other students require multiple revisits of a particular concept for them to be able to grasp it.

The solution to this problem is to tailor the education experience for each particular student’s individual preferences and pace. This will enable the students in the long run in getting a deeper understanding of concepts.

The Idea

Usually learning platforms have struggled to maintain the balance between super specific course curriculum and covering the diversity of the students that come in to study. Large schools fail to identify individual talent and nurture them as they have to focus on getting the entire group up to speed, while contemporary learning platforms fail to cover the experience of the entire user group.

Our solution solves both of these problems by taking any concept or idea and restructuring it in a user customised, friendly way thereby fast-tracking the students’ understanding

Enabling the future

Each student is unique in their own way and each one thinks differently. Every student has their own creative ideas. Our solution enables them to look at problems through their own looking glass and explore new unexplored ideas within. By enabling the younger generation with better education, The future will be secure and the quality of the research output will be immense.

Flow diagram

WorkFlow

• The coursework / content to be taught is connected with a general LLM that serves up the data when required
• A data conversion agent then takes the data generated from the RAG and converts it to the users specific preference and displays the content.
• The user is then measured on how well the content was consumed and how efficient the model was. The efficiency parameters are updated on the data conversion agent and the agent gets attuned to the specific user preferences.
• For increased fetches the agent gets updated and gets better suited to the users pace.