Teaching Writing Across the Curriculum with AI
Writing Across the Curriculum Assignments, Classroom Activities, and Educational Resources for Higher Education
Students begin by collaboratively analyzing implementations of text generators, such as Christopher Strachey’s Love Letter Generator. Next, students work in groups to create non-executable conceptual programs designed to produce new instances of chosen genres.
Students utilize Large Language Models (LLMs) such as ChatGPT to assist in the peer review process by generating responses to common peer review prompts.
Students engage in prompt engineering by iteratively refining and tweaking the set of instructions given to a Large Language Model (LLM). Through a collaborative workshop format, students work in groups to analyze the AI’s output, discuss its strengths and weaknesses, and propose improvements.
Students engage in sustained conversations with AI chatbots like ChatGPT, analyzing the bots’ writing and thinking patterns. The assignment prompts students to consider the ethical implications of using AI chatbots, including issues related to plagiarism, authorship, and the potential displacement of human labor.
This assignment serves as a beginner-level programming exercise that introduces students to the concept of iteration using for loops. By writing code that repeats a word or phrase 50,000 times, students gain a fundamental understanding of how loops work in programming.
The assignment introduces an “AI Standards of Conduct” framework that differentiates between AI-assisted and AI-generated writing. The assignment encourages students to reflect on the ethical implications of using AI in writing.
Students explore and reflect on algorithmic accountability through a speculative scenario where an AI called Professor Bot grades entrance essays.
Students reflect on their personal histories and preferences by participating in an interactive survey. These responses are then analyzed using text generation models to explore how AI interprets and predicts social identities, highlighting the biases and assumptions inherent in AI technology.
Students use accessible language models to generate parts of their term papers. This hands-on experiment helps students explore the capabilities and limitations of AI in writing, as well as the ethical dimensions of using such tools.
By comparing AI-generated outputs with their own work and reflecting on these comparisons, students develop enhanced metacognitive skills, which are crucial for their personal and professional growth.
Students generate text using both analog cut-up techniques and a simple Markov procedure. This exercise introduces them to found art, encouraging them to critically examine issues of property and power in the context of generative text and its sources.
Students use the JavaScript library Tracery to create procedural texts that generate new content based on predefined rules and word banks. This approach explores the power of randomness and variability in text generation, akin to advanced Mad Libs.
This assignment helps students grasp core aspects of narrative theory by requiring them to generate multiple variations of an underlying story. Students use Curveship-js, a JavaScript framework, to implement their narrative variations.
Students create “spells,” or poems based on a wish, and then use a large language model (LLM) to generate a corresponding AI-created spell.
This assignment engages students in the practice of procedural creativity by encouraging them to play with, hack, or build text generators.
Students compare AI-generated responses to human-written responses, examining differences in style, clarity, and effectiveness.
Students generate a paper focused on a highly specific and recent text technology and reflect on how LLMs influence the writing process and their own writing practices.
Students are tasked with training a large language model (LLM) using a small set of sample tweets to generate content in the style of specific Twitter accounts. After generating tweets, students conduct a rhetorical analysis of the generated content.
Students use AI tools to better comprehend and analyze complex texts, asking AI questions about the texts to deepen their understanding and engagement. Students use AI as a collaborative tool to enhance their own writing process while ensuring their original contribution remains substantial.
Students choose a dataset to train their language model and analyze the generated output to identify patterns and new meanings. They write a reflective essay to critically consider the affordances, challenges, and generative potential of large language models (LLMs).
Students manually apply an algorithm to generate a Markov Chain from a given text extract, providing a concrete and interactive way to understand probabilistic language generation.
Students analyze how the AI processes and generates text, providing a practical understanding of natural language processing (NLP) concepts. They critically evaluate the AI-generated responses and reflect on the potential applications and limitations of using AI in technical communication.
Students collaborate in small groups to produce a creative work and then write a reflective piece about their contribution to the project and the group dynamics.
Utilizing a threaded discussion platform, students experiment with AI image-making programs, focusing on the verbal prompts they create. Students are evaluated not only on the quality of their prompts but also on their support and encouragement of other writers.