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# Learning Objective Achievement
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Learning Objective Achievement is an AI solution that creates high-quality assessments that use the same source(s) to determine if students have achieved learning objectives, perhaps through Q&A, critiquing a response, etc.
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## Features
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- Generative AI and LangChain integration to process sources and create assessments and answer keys
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- No programming background required for usage
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- Runs on Google Colab, with no additional installations needed
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## Getting Started
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To use Learning Objective Achievement, simply open it in Google Colab. It will take a few minutes to spin up and automatically install the required packages.
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## Contributing
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To contribute to the project, please fork the repository and submit a pull request. Our community is supportive, and we provide training and classes if you're new to any of the frameworks used in the project. Everyone is welcome to contribute, as we believe participating in data science and AI projects is an excellent way to learn.
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## Community Guidelines
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We aim to create a welcoming and inclusive community where everyone can feel comfortable and valued, regardless of skill level, background, ability, or identity. To ensure a positive atmosphere, please adhere to our code of conduct and community guidelines.
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## Meetings
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- Sprint Planning & Retrospective: Mondays and Fridays at 10:30 am
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- Demos: Fridays at 3 pm
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## Additional Resources
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- LangChain documentation
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- Introduction to transformers and generative AI on our [YouTube channel](https://www.youtube.com/channel/UC8C2_3L5gR9qLmL7rmb2BdQ)
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- AI Summer and AI Winter sessions (free and open to all)
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## Reporting Issues
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If you encounter a bug, please submit an issue and label it with "Bug." To escalate the issue, email [[email protected]](mailto:[email protected]).
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## Contact Information
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- Organization: Data Science Institute at Vanderbilt University
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- Program: Data Science for Social Good
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- Main Email: [[email protected]](mailto:[email protected])
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- Staff Lead: [[email protected]](mailto:[email protected])
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