Composing with AI

Chapter Abstracts

The chapters in this collection were originally drafted in 2023 and 2024, often reporting on projects that relied on earlier versions of GPT applications (typically ChatGPT 3.5 and 4.0 or Claude 2.0). However, no chapter relies entirely upon the specifics of any given GPT application or version. The works presented here have been reviewed and edited in 2025, and the insights and findings presented in this collection remain relevant and continue to convey important findings. In the computers and writing field, we have a long history of producing works about technological innovations that don't rely solely on specific new products so much as investigating the processes and implications of new communication technologies on writing practices (see, e.g. Hawisher and Selfe, 1989; Holdstein and Selfe, 1990; Hawisher and Selfe, 1991; Hawisher and LeBlanc, 1992). Our aim in this collection is to continue that tradition of critical analysis of new technologies without falling into the trap of reporting on or assessing specific instances or applications generative AI.

Histories: C&W Approaches to Disruptive Technologies

What We already Know: Generative AI and the History of Writing With and Through Digital Technologies
Anthony Atkins and Colleen A. Reilly

Atkins and Reilly examine three central themes present in both past and current literature in the fild of computers and writing: the challenges posed to conceptions of writing and authorship; the access and accessibility implications of information and communication technologies; and the degree to which technologies reveal and mask their mediation of content. The scholarship addressing these themes is as relevant for working and teaching with AI just as it was for working with MOOs (English, 1998), Wikipedia, and a myriad of other information and communication technologies. They argue that the field can and should harness this scholarly legacy to help faculty and students navigate the evolving context for writing, composing, editing and design necessitated by the introduction of generative AI.
GPT Applications: All Vendors/All Versions

The Black-Boxed Ideology of Automated Writing Evaluation Software
Antonio Hamilton and Finola McMahon

Hamliton and Finola draw on the decades-long conversation regarding automated writing evaluation technologies (AWE). These technologies are often heavily black-boxed; consequently, instructors, students, and writers, often use these technologies without fully understanding their function and their impact on writing. In a study of current AWE applications, the authors found three central themes: 1.) The user's technical illiteracy being used against them to prevent a full understanding of the programs prior to purchase, 2.) The programs' websites obscuring details about how the algorithms function, and 3.) Black-boxing as an appeal to current-traditional rhetoric and the use of static abstractions in writing feedback.
GPT Applications: All Vendors/All Versions

Policy: Programs & Publications

Drafting a Policy for Critical Use of AI Writing Technologies in Higher Education
Daniel Frank and Jennifer Johnson

Frank and Johnson present a dialectical conversation reflecting on the incorporation of generative AI tools in the writing classroom. They trace the development of the UCSB Writing Program's Policy on ChatGPT and AI Writing, which aims to provide ethical guidance and support for faculty and students in using these technologies. Through faculty workshops and discussions, Frank and Johnson shaped the key principles of the UCSB policy: 1) Integrating AI as one of many supportive feedback tools; 2) Promoting academic integrity through transparency about AI usage; 3) Fostering critical thinking about the tools' biases and limitations; 4) Cautioning against over-reliance on AI detection.
GPT Applications: Initially ChatGPT 3.5 but applies to All Vendors/All Versions

A Textual Transaction: The Construction of Authorship in AI Policy Statements
James P. Purdy

Purdy examines updates to submission policies of academic journals and publishers prompted by the emergence of generative AI. In arguing that generative AI cannot be listed as an author, these policies define what authors, and by extension writing, are and should be. In this chapter, these policy responses are situated in to relation to early computers and writing scholarship, including Burns (1983), Herrington and Moran (2001), and Baron (2000). Based on close reading and content analysis of ten journal and publisher AI policies published within six months of ChatGPT’s initial public release, Purdue found policies to limit authors to people, allow inclusion of some AI-generated content under certain conditions and citation guidelines, and frame writing as a textual product performing transactional functions.
GPT Applications: All Vendors/All Versions

Reports from the Field: Classes & Students Using AI

Reconsidering WritingPedagogy in the Era of ChatGPT: Results of a Usability Study of ChatGPT in Academic Writing
Lee-Ann Kastman Breuch, Asmita Ghimire, Kathleen Bolander, Stuart Deets, Alison Obright, and Jessica Remcheck

Inspired by the question "How are undergraduate students understanding ChatGPT as an academic writing tool?" This chapter shares results of a study of student impressions of ChatGPT conducted at a large midwestern university. Thirty two undergraduate students participated in a contextual usability inquiry study, completing five tasks using ChatGPT and rating the outputs in terms of expectations, satisfaction, credibility, and relevance. Students consistently rated ChatGPT texts as high in relevance and expectations, but lower in terms of satisfaction and credibility. Based on the results of the study, the authors advocate a “critical AI literacy” approach that invites students to use ChatGPT and critically evaluate its texts.
GPT Applications: ChatGPT 3.5

ChatGPT is Not Your Friend: The Importance of AI Literacy for Inclusive Writing Pedagogy
Mark C. Marino

Marino reflects on his discoveries from a summer intensive first-year writing course, later revised for sections of an advanced writing course, at the University of Southern California in 2023 which focused on Machine-assisted Writing. The course focused on both understanding and using ChatGPT 3.5 and other LLMs for everything in class, from generating a start of class check-in question, which it did quite well, to augmenting our research methods, which had mixed results. Ultimately these experimental lessons revealed two important findings: AI tools present yet another divisive wedge between the digitally literate haves and the less literate havenots, and as students' understanding of these systems increases, the potential for productive, creative, and critical use of these tools likewise increases. This chapter details the experimental assignments, in class work, and theoretical basis that led to those findings.
GPT Applications: ChatGPT 4.0, but assignments apply to All Vendors/All Versions

Mind the Gaps: Evaluating Student Perceptions on GenAI and the Future of Writing
Jeanne Law, James Blakely, John C. Havard, and Laura Palmer

This chapter presents findings from a study conducted at Kennesaw State University (KSU) aimed at illuminating first-year students' perceptions of gen-AI and measuring student attitudes toward gen-AI in both academic and personal writing contexts. Though initial quantitative findings indicated a high awareness of gen-AI among students, many respondents indicated they never use gen-AI for academic purposes. Additionally, a significant portion of students expressed uncertainty about the future role of gen-AI in writing, and opinions remained divided on the ethics of AI use in academia. Technical communications students were more accepting of gen-AI than first-year writing students across contexts, reflecting their comfort integrating new technologies in their work. This study underscores the need for ongoing dialogue about AI and the development of pedagogical strategies to address the ethical and practical implications of gen-AI in education.
GPT Applications: All Vendors/All Versions

Style: Comparing AI and Human Approaches to Style

LLMs for Style Pedagogy
Christopher Eisenhart

Eisenhart examines the capacities of generative AI applications to engage in the editing and revision work of style by putting ChatGPT 3.5 through the exercises presented in Joseph Williams's Style: Lessons in Clarity and Grace. The chapter provides an analysis of the findings, including places where CGPT performed as well as a human student would, and also those places where its struggles were similar to those of many first year writing students, especially where context and inference are required for successful revision.
GPT Applications: ChatGPT 3.5

Stylistics Comparison of Human and AI Writing: A Snapshot in Time
Christopher Sean Harris, Evan Krikorian, Tim Tran, Aria Tiscareño, Prince Musimiki, and Katelyn Houston

The study represented in this chapter compares the stylistics of human writing to AI writing. Harris and his student co-authors collected student-written assignments and assignment prompts, then used OpenAI's ChatGPT and Google Bard (rebranded as Gemini) to write assignments to fulfill the same prompts. The resulting texts were structurally analyzed using a modified version of Edward PJ Corbett's stylistics exercises republished in Style and Statement. The team also compared a sample of human-written texts and AI-generated texts using a modified version of Richard Haswell's (1984) Intra-Subject Paired Comparison rubric. This study provides humanities-driven evidence that identifies key stylistic features of collegiate human and AI writing. The data from this study can be replicated to create a large language stylistics model useful for timestamping and understanding how AI writing platforms and their trainers stylistically assemble texts, for understanding how humans and computers write differently, and for ascertaining whether computers can stylistically write belter than humans given student-generated prompts.
GPT Applications: ChatGPT 3.5, Bard 1.0

Multimodal Composing: AI Text-to-Image Applications

Composing the Future: Speculative Design and AI Text-to-Image Synthesis
Jamie Littlefield

This chapter proposes speculative design as a critical method for engaging with AI text-to-image synthesis. Large Language Models (LLMs) like ChatGPT and text-to-image synthesis tools such as Midjourney are fundamentally rooted in the past, trained on vast datasets that encapsulate historical texts, images, and multimedia. AI’s entrenchment in the past generates an algorithmic resistance to change, creating synthetic output that uncritically re-creates values and social structures. Drawing on case studies from urban communication, the chapter demonstrates how speculative design can help communicators notice and challenge AI’s entanglement with the past. In the writing classroom, speculative design practices can provide students with concrete approaches to analyzing and composing AI-generated images that demonstrate awareness of the ways AI reproduces the past.
GPT Applications: DALL-E 2, DALL-E 3, Midjourney (2023)

Teaching about Technology Bias with Text-To-Image Generative AI
Sierra S. Parker

This chapter analyzes biases undergirding and (re)produced by text-to-image generative AI compositions, presenting a critical approach that engages technology bias and visual pedagogies of artificial intelligence for the rhetoric and writing classroom. By analyzing examples of text prompt inputs and image outputs from two AI models (Dall-E 2 and Bing Image Creator), Parker illustrates how bias informs the process of composing AI images through not only the user but also the technology. Presenting examples of the ways that AI images direct the viewer, the author offers strategies for using this technology meaningfully in the classroom as an object of critical analysis, providing guiding prompts for classroom activities and possibilities for scaffolding AI image content within rhetoric and writing courses.
GPT Applications: DALL-E 2, Bing Image Creator (2023)

Theory: How AI Impacts Rhetoric and Ethics

Teaching Knowledge Labor and Literacy for the Age of AI and Beyond with Rhetorical Information Theory
Patrick Love

Because ChatGPT is a black box that appeared to the vast majority of onlookers as without precedent, excitement and anxiety freely mix in reactions to its existence and progress. As such, scholars and faculty need both theory and practice to inform new pedagogical decisions in composition and other writing classes. This chapter presents key concepts from Information Theory that inform Generative AI’s function as a chatbot that "generates" responses by selecting and remixing from training data to produce responses to user requests. Namely, this chapter presents the DIKW pyramid, a graphic that Information Theory uses to rationalize the theoretical relationships between data, information, knowledge, and wisdom as part of teaching machines to participate in knowledge-work. This chapter demonstrates connections between DIKW, circulation theory, and active learning pedagogy in order to present a version of the DIKW pyramid crafted for composition classes and rhetoricians to use as a boundary object to further connections and partnerships between humanities and STEM disciplines, professionals, and students. The chapter concludes with examinations of how the pyramid and its associated language can help faculty discuss ChatGPT and LLMs with classes and start forming their own inquiries and assignments that use or challenge LLMs in their own work and with students.
GPT Applications: All Vendors/All Versions

Interfacing Chat GPT: A Heuristic for Improving Generative AI Literacies
Desiree Dighton

This study explores the implications of ChatGPT and similar generative AI technologies in the context of writing studies, emphasizing the need for developing critical AI literacies. The research draws on historical and contemporary theories of interface design and usability, including the work of Selfe and Selfe, Mel Stanfill, and Corrine Jones, to analyze how these technologies shape user interactions and perpetuate dominant cultural values. The interface of ChatGPT, particularly its conversational design, is examined for its affordances and constraints, revealing how it subtly directs user behavior and engagement. By integrating heuristic development and analysis into classroom practices, this study aims to enhance students' understanding of AI technologies, fostering critical engagement and agency. The findings highlight the importance of situating AI tools within broader socio-cultural contexts, advocating for a more inclusive and reflective approach to integrating AI in educational settings.
GPT Applications: ChatGPT 3.5, ChatGPT 4.0, applies to All Vendors/All Versions