Improving E-Learning Experience with Doubt Clarification

In the constantly growing environment of online education , there is a lack in doubt clarification and it reduces the efficiency of elearning systems. This project improve it Using LLM models

Comment

Inspiration story ( must read )

As I sat in the classroom, my friends and I were engrossed in a technical discussion. The topic of the day was how to learn about coding, a question that often puzzled us. As they turned to me for answers, I pondered for a moment before responding.

"Well," I began, "one way to learn about coding is to watch tutorial videos on any online platform. They can be incredibly helpful. And if you have doubts, just search for answers on the web. There's a wealth of information out there."

My friends nodded, but one of them voiced their concerns. "That sounds great, but sometimes our doubts are so specific that we can't find clear answers online. And even when we do, there's often a delay in getting responses."

It was in that moment that a spark of inspiration ignited within me. I thought, "What if there was a way to create an environment where people could learn to code without the frustration of unanswered questions and delays?" This is where AI could play a pivotal role.

What it does

Creating a chat environment powered by AI, a place where learners could ask questions, clarify doubts, and receive instant responses. It was a venture driven by the desire to make coding education more accessible and interactive. Little did I know that this idea would take me on an exciting adventure into the world of artificial intelligence and education, forever changing the way we learn and master the art of coding.

How we built it

Our system uses a neural network-based large language model (LLM) and Prompt Engineering to analyze and reply to learner queries quickly, independent of time or volume. Our development reduces the irritation and delays associated with doubt clarification by offering accurate answers, explanations, or further resources immediately, therefore it improves the entire e-learning experience.

LLMs are pre-trained on massive text datasets, giving them an in-depth understanding of language. They understand and generate wording that is similar to that of humans, making them excellent for natural language processing tasks. On the other hand, prompt engineering is required to take advantage on this potential for doubt resolution. It involves developing specific questions or prompts that direct the LLM to produce the desired responses.

Challenges we ran into

  • implementation of UI
  • API Response
  • Animation of response and request in UI
  • Prompt engineering for response

Accomplishments that we're proud of

Prompt Engineering

Making the AI the response according to the doubt is a key requirement which as been accomplished

What we learned

Learned about prompt engineering and how to handle LLM models

What's next for Improving E-Learning Experience with Doubt Clarification

AI based flashcards :

Provides A Flashcards in the chat environment as the recommendation of related courses in our e-learning platform

Built With

Share this project:

Updates