Professor Bot: An Exercise in Algorithmic Accountability
Students explore and reflect on algorithmic accountability through a speculative scenario where an AI called Professor Bot grades entrance essays.
Teaching Writing Across the Curriculum with AI
Writing Across the Curriculum Assignments, Classroom Activities, and Educational Resources for Higher Education
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.
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 engage in hands-on activities where they create prompts, analyze the outputs generated by LLMs, and reflect on the outcomes.
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.
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 translate a complex policy document into plain English, enhancing their ability to simplify and clarify technical content for broader audiences.
Students research various text analysis and summarization tools, allowing them to critically evaluate the effectiveness and accuracy of these technologies in summarizing technical documents.
Students generate prompts, collect consecutive responses from ChatGPT, and analyze these iteration. By summarizing, synthesizing, and editing AI responses, and conducting library research, students examine the ethical implications of AI in writing.