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Use Case

Automated content creation for e-learning

Generative AI models like LLMs (Language Learning Models) and image generators can be vital in automating content creation in e-learning platforms. LLMs, like OpenAI's GPT-3, can assist in the generation of educational material tailored for various learning levels, topics, and languages. For example, curating reading material for primary grade science or generating complex mathematical problem sets for university students.

Image generators can create visual aids to support theoretical concepts which increases comprehension. For example, generating images to better illustrate molecular structure in chemistry lessons or historical events for social studies. Furthermore, LLMs can be used to create AI teaching assistants providing real-time explanations to students' queries. Rigorous assessments can be automated too, with LLMs developing, marking and providing feedback on examinations. Hence, generative AI aids in customizing and enhancing the e-learning experience while significantly reducing manual labor.

How to build with Clevis

Text Input

Get the topic for the e-learning content

Prompt ChatGPT

Prompt ChatGPT to generate an outline for the content based on the topic

HTTP Request

Fetch relevant reference materials from external e-learning resources

Generate Image (DALL-E)

Generate visual aids or illustrations related to the content

Display Output

Display the final e-learning content for review and modification

This description pertains to an example application called 'Automated Content Creator' that can be built using a tool named Clevis. The purpose of this application is to automate the creation of e-learning content - a task many developers within this sphere can undertake more efficiently with Clevis.

The application operates in a series of steps. Firstly, the application invites users to input a topic for the e-learning content, which is the basis for the content generation process. In the following step, it employs ChatGPT, an AI model developed by OpenAI, to generate an outline for the content based on the provided topic. This AI model uses machine learning algorithms to analyze and understand the topic, producing a detailed outline for the content accordingly.

In the next stage, the application uses HTTP requests to fetch relevant reference materials from an assortment of external e-learning resources. These resources bolster the base provided by ChatGPT, granting additional depth and accuracy to the content. Subsequently, DALL-E is implemented, another AI from OpenAI, which is capable of generating visual aids or illustrations relevant to the content.

Finally, the combined output - curated content, additional references, and visual aids - is displayed for review and modification to users, creating a fully automated and customizable e-learning content creation process.

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