Teaching Writing Across the Curriculum with AI
Writing Across the Curriculum Assignments, Classroom Activities, and Educational Resources for Higher Education
Students generate predictions using ChatGPT and critically assess the output. The assignment emphasizes an analytical approach to evaluating hypothetical scenarios generated by ChatGPT. Students must go beyond simple fact-checking and use reasoned analysis to diagnose the plausibility of the predictions.
Students analyze how facts are assembled into an argument, focusing on the validity of the connective logic. They are encouraged to look beyond the individual facts and assess whether the conclusions drawn are logically supported by the premises.
Students collaborate with peers from different disciplines, using AI text generators. The collaboration enhances their understanding of writing styles across various fields and fostering diverse perspectives in their work.
Students are provided with AI-generated texts relevant to the course topics and are asked to comment, review, and expand on these texts using text-editing software.
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 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 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.
This assignment engages students in the practice of procedural creativity by encouraging them to play with, hack, or build text generators.
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 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 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.
Students use AI-based applications to assist in proofreading and improving their written work, fostering familiarity with modern technological aids in professional writing.
Students select and read medical journal articles, draft summaries, and revise them, allowing for progressive skill development. The assignment incorporates the use of AI writing systems throughout the process.
Students research various text analysis and summarization tools, allowing them to critically evaluate the effectiveness and accuracy of these technologies in summarizing technical documents.