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ncwilson78A library of customizable system prompts for educational GenAI agents
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A dynamic repository of customizable system prompts for Generative Pre-trained Transformers (GPTs), tailored for educational applications. It serves educators and learners by providing structured prompts to guide AI interactions, support pedagogical goals, and enhance learning experiences, offering a valuable resource for creating customized educational GPT agents.
How It Works
This library offers a collection of system prompts designed for educational use, categorized for educators and learners. Each prompt includes a summary of its purpose, defined learning objectives, and a basic interaction structure. Users can directly copy these prompts and integrate them into their custom GPT tools, facilitating experimentation and adaptation for specific teaching contexts.
Quick Start & Requirements
To use a prompt, copy it from the "Copy This Prompt" code block and paste it into your custom GPT tool. No specific installation or technical prerequisites are mentioned beyond the ability to utilize custom GPT tools. A submission form is available for contributing prompts if you are unfamiliar with GitHub.
Highlighted Details
Maintenance & Community
Contributions are welcomed from educators, developers, and educational technologists. Interested parties can fork the repository on GitHub, make additions or improvements, and submit a pull request. Alternatively, prompts can be submitted via a dedicated online form. The project is associated with Harvard University's Office of the Vice Provost for Advances in Learning.
Licensing & Compatibility
Each prompt is licensed for reuse, but users are advised to check the specific licensing requirements for each prompt, as different models have varying conditions. Compatibility for commercial use depends on the individual prompt's license.
Limitations & Caveats
Large Language Models can produce unexpected or unsuitable responses. Users must tailor prompts to their specific student needs and teaching contexts, and exercise caution by verifying the appropriateness and accuracy of LLM outputs.
1 year ago
Inactive