This assignment, by Joshua J. Wells, is from the TextGenEd: Continuing Experiments collection on the WAC Clearinghouse website.
The summary from the site explains:
The behaviors of textual AI tools are opaque to most college students, and misunderstandings about how such tools function are rampant. The term “artificial intelligence” can mislead students into believing that such tools contain actual knowledge, when instead those tools create statistical models of textual associations. This exercise introduces students to the concept of textual tokens in AI training data, which form the base data from which statistical associations are modeled. This helps students to visualize how AI processes function to build textual answers to prompts.
Key Features of This Assignment
- Understanding Tokenization
- Students recognize how word choices and phrasing impact AI-generated responses by examining how AI breaks text into tokens.
- Effective Prompt Design
- It highlights strategies for crafting precise prompts that yield clearer, more relevant AI-generated content, improving students’ ability to communicate their writing goals.
- Critical Analysis of AI Output
- Students evaluate AI-generated text, gaining deeper engagement with issues of coherence, accuracy, and rhetorical effectiveness.
Find the full version of this resource in the TextGenEd: Continuing Experiments collection.