Text2Vid EduCraft: Personalized Learning Video Generator

Inspiration

Every learner has different prior knowledge about a specific topic, and creating content tailored to individual needs can be a challenge. This project aims to solve the problem by generating curated videos to effectively impart knowledge.

What it does

Text2Vid EduCraft: Personalized Learning Video Generator utilizes advanced algorithms to analyze text book paragraphs and generate personalized learning videos. By tailoring the content to the individual's prior knowledge and learning style, it enhances comprehension and retention.

How we built it

We leveraged LLM capabilities for text-to-text generation models by converting the input paragraph into categorizing the three difficulty levels: EASY, MEDIUM, and HARD. This categorization allowed us to tailor the content to the learner's proficiency level. Additionally, we utilized text-to-image generation techniques to obtain meaningful images that complemented the text. These images were then incorporated into a video script to enhance the visual appeal and educational value of the videos.

Challenges we ran into

Challenges we encountered included occasional delays in image generation, as well as instances where the generated images were not accurately aligned with the content. Additionally, we faced errors when attempting to generate images in parallel. Similarly, we encountered difficulties with video script generation, requiring adjustments to the prompt for accurate script generation.

Accomplishments that we're proud of

We're proud to have developed a system that can dynamically adapt content to suit the unique needs of each learner. Our personalized learning videos have shown promising results in improving knowledge retention and engagement

What we learned

Through this project, we gained a deeper understanding of the complexities involved in personalized learning and the potential of leveraging technology to address them. We also honed our skills in NLP, video generation, and user-centric design.We also explored various video generation models and SDKs invluding MoviePy.For the audio generation, we leveraged upon Amazon Polly.

What's next for Text2Vid EduCraft: Personalized Learning Video Generator

In the future, we aim to further enhance the algorithm's ability to tailor content by incorporating user feedback and performance analytics. We also plan to expand the platform to support a wider range of educational materials and subjects, empowering learners of all ages and backgrounds.Also, we plan to add support for other languages targeting global learners.

Built With

• amazon-web-services
• claude
• moviepy
• polly