In the ethical considerations category, assignments are split between two primary foci—the first engages students in the institutional ethics of using LLMs in undergraduate classrooms and the second attends to the ethical implications of LLMs and their outputs.
Analyzing Class Datasets: A Writing Philosophy Case Study for First-Year Composition
Kate Navickas & Laura Davies
Cornell University, SUNY Cortland
This is a two-part assignment sequence that asks students to explore their own beliefs about how generative AI affects writing and writers. The first assignment asks students to write a 1-page creative non-fiction “Writing Philosophy”; then, we collate all students’ writing philosophies and analyze them as a class dataset in the Writing Philosophies Case Study assignment. The assignment sequence challenges students to put their own experiences with AI in conversation with both scholarship on writing and their peers’ experiences.
Calvin Pollak
University of Washington
A key aspect of social justice in technical communication is avoiding socially biased language, which may negatively affect our audiences or relevant stakeholders for our communications. Large language models (LLMs) such as Copilot are increasingly being deployed to generate technical communication texts, whether in whole or in part. What are the potential social implications of this technology’s deployment, given the biases that exist in LLM-generated communication? To explore this question, in this activity we probe Copilot for social biases by giving it a series of prompts engineered to unearth bias and critically analyzing its responses.
Morgan Banville
Massachusetts Maritime Academy
This assignment asks undergraduate students to co-create a classroom policy about generative artificial intelligence (GenAI). Through empowering students to make decisions in the classroom, this collaborative approach contributes to fostering both a community and a better understanding of the ethical, social, environmental, and political impacts and consequences of GenAI.
Three-Stage Metacognitive Reflection Framework for AI Engagement
Liping Yang and Michael Harker
Georgia State University
In this assignment, students pause to engage in micro-level metacognitive reflection during AI interactions. They will examine how they exercise judgment as writers, the strategies they use to build and organize knowledge, and how AI models support or challenge those strategies. Students will also reflect on AI performance, its strengths and limitations, and how its output shapes their writerly identity. These reflections help students navigate the balance between human and machine agency, build awareness of their writing habits, and identify areas of growth. Ultimately, the assignment cultivates AI literacy, encouraging intentional, strategic use of AI rather than passive acceptance or rejection.
