This assignment, by Natalie Goodman, is from the TextGenEd collection in the WAC Clearinghouse Repository.
The abstract from the site explains:
This assignment first tasks students with creating their own text generator using a premade module and then asks them to reflect on the experience of directing an LLM-generated composition. Students will choose a dataset to train their LLM, examine its output to identify patterns and new meanings that may emerge, and write a reflective essay that critically considers the affordances, challenges, and generative potential of LLMs. Originally taught in an upper-level writing and media class, this project is designed to accompany a theoretical exploration of disability studies and queer theory, but could be adapted for other contexts and disciplines. While a background in computer science is not necessary for students or teachers, this assignment will require enough time for trial and error as students troubleshoot their LLMs.
Key Features of This Assignment
- Creating a Custom Text Generator
- Students are tasked with creating their own text generator using a premade module. This hands-on experience allows them to engage directly with AI technology, enhancing their understanding of how text generators function and the complexities involved in directing AI-generated compositions.
- Data Set Selection and Pattern Analysis
- Students choose a dataset to train their language model and analyze the generated output to identify patterns and new meanings. This exercise helps them develop critical skills in data analysis and interpretation, while exploring the generative potential of AI in producing novel text.
- Reflective Essay on AI Collaboration
- Students write a reflective essay to critically consider the affordances, challenges, and generative potential of large language models (LLMs). This component encourages deep reflection on the collaborative nature of working with AI, integrating theoretical perspectives from disability studies and queer theory.
Find the full version of this assignment at the WAC Clearinghouse.