For English as an Additional Language (EAL) students, generative AI (GenAI) offers meaningful support for writing in English, while also introducing a new set of challenges. Supporting EAL students in developing AI literacy is crucial to their growth as confident, adaptable writers, and writing center tutors are uniquely positioned to facilitate this development. This case study explores the experiences of undergraduate Brazilian international students at a small liberal arts college who received writing feedback from both peer writing center tutors and ChatGPT. Findings indicate that students valued the human connection, contextual understanding, and rhetorical support offered by peer tutors, while turning to ChatGPT for immediate, nonjudgmental assistance, particularly in navigating multilingual challenges. The study offers insight into how peer writing tutors can thoughtfully leverage GenAI to support multilingual writers.

This article investigates the rhetorical means used by EFL university students in interactions with ChatGPT with emotional prompts. It has been found that most participants do not construe the interaction with the bot as a traditional communicative situation, and do not frame the bot as a humanlike agent. However, after being prompted to use emotional appeal, the participants mapped the features of human-human communicative situation without mapping the perception of the interlocutor as a human being.

This article presents a conceptual framework for integrating artificial intelligence (AI) to enhance communication skills in educational and business settings. By examining the dual role of AI as both an enabler and a challenge, the article highlights AI’s capacity for personalized learning, skill development, and efficiency in communication tasks. It also addresses potential issues such as academic integrity, data reliability, and ethical considerations. This framework aims to guide institutions and organizations in adopting AI responsibly, ensuring that human-centered communication remains integral to AI-enhanced environments.

This article argues that ethical authorship is essential for the ethical use of artificial intelligence (AI). It examines tensions that historical understandings of authorship have created as instructors and students alike navigate AI technologies. Given these tensions, this article proposes a definition of “ethical authorship” and uses de Colle and Werhane’s moral motivation framework to outline how instructors can use ethical authorship and moral motivation to encourage students’ ethical AI use.

Critical thinking and problem-solving are essential skills in management education. ChatGPT and other AI-assisted writing tools might disrupt conventional tools like essay writing and case-study analysis. The project incorporates bibliotherapy-inspired usage of ChatGPT and critical thinking and problem-solving frameworks to make students identify and solve real-life problems like social media addiction or time-management skills. ChatGPT is used as an assistant, coach, and/or motivator in the project. Students’ experiences are shared as blog posts. The impact of the project on developing critical thinking and problem-solving skills is measured by a post-and-then-pre survey questionnaire.

Drawing upon a framework of “assemblage thinking,” this article offers an approach to considering artificial intelligence (AI) and ethics that seeks to think relationally across the positions occupied as educators and students at a business school. To complement discussions of assemblage and examinations of ethics in the AI era, we draw upon the perspectives of a relatively understudied population in this conversation—students themselves navigating AI and writing within a business-focused context—and extend assemblage thinking to capture important thought toward the future of business communication, pedagogy, ethics, and AI.

The public release of ChatGPT in 2022 ushered in a new era, affirming the present reality of AI-assisted writing and the critical role business instructors play in preparing students. This study presents the results of a pedagogical experiment. Specifically, it evaluates strategies for integrating and teaching about AI in the business communication classroom, focusing on the impact of generative AI on students’ understanding of business writing principles and how different levels of engagement with AI influence students’ critical AI literacy and attitudes toward AI-assisted writing in the workplace.

This article presents the ongoing conversation about generative AI guidance and policy in higher education. The article examines syllabus policies, including analyzing sentiment, emotion, and common themes in GenAI policies. Findings show that policies should be audience-focused, clearly written, and grounded in strategies to promote ethical AI use in academia and the workforce. Practical tips for policy writing and sample policies are provided.

• This study explores GenAI's role in multimodal composition, including Adobe Firefly and DALL·E. • GenAI reshapes the composition stages of invention, designing, and revising. • Despite its limitations, GenAI offers alternative solutions to wicked problems. • Post-GenAI use, students critically revise and iterate their compositions. • The study contributes to future research and teaching of AI-assisted composition. This study explores the integration of generative artificial intelligence (GenAI) design technologies, including Adobe Firefly and DALL·E, into the teaching and learning of multimodal composition. Through focus group discussions and case studies, this paper demonstrates the potential of GenAI in reshaping the various stages of the composition process, including invention, designing, and revising. The findings reveal that GenAI technologies have the potential to enhance students’ multimodal composition practices and offer alternative solutions to the wicked problems encountered during the design process. Specifically, GenAI facilitates invention by offering design inspirations and enriches designing by expanding, removing, and editing the student-produced design contents. The students in this study also shared their critical stance on the revision process by modifying and iterating their designs after their uses of GenAI. Through showcasing both the opportunities and challenges of GenAI technologies, this paper contributes to the ongoing scholarly conversations on multimodal composition and pedagogy. Moreover, the paper offers implications for the future research and teaching of GenAI-assisted multimodal composition projects, with the aim of encouraging thoughtful integration of GenAI technologies to foster critical AI literacy among college composition students.

In this paper, we demonstrate how studying the rhetorics of ChatGPT prompt writing on social media can promote critical AI literacies. Prompt writing is the process of writing instructions for generative AI tools like ChatGPT to elicit desired outputs and there has been an upsurge of conversations about it on social media. To study this rhetorical activity, we build on four overlapping traditions of digital writing research in computers and composition that inform how we frame literacies, how we study social media rhetorics, how we engage iteratively and reflexively with methodologies and technologies, and how we blend computational methods with qualitative methods. Drawing on these four traditions, our paper shows our iterative research process through which we gathered and analyzed a dataset of 32,000 posts (formerly known as tweets) from X (formerly Twitter) about prompt writing posted between November 2022 to May 2023. We present five themes about these emerging AI literacy practices: (1) areas of communication impacted by prompt writing, (2) micro-literacy resources shared for prompt writing, (3) market rhetoric shaping prompt writing, (4) rhetorical characteristics of prompts, and (5) definitions of prompt writing. In discussing these themes and our methodologies, we highlight takeaways for digital writing teachers and researchers who are teaching and analyzing critical AI literacies.

In writing assessment, there is inherently a tension between authenticity and practicality: tasks with longer durations may more closely reflect real-life writing processes but are less feasible to administer and score. What is more, given total testing time, there is necessarily a trade-off between task duration and number of tasks. Traditionally, high-stakes assessments have managed this trade-off by administering one or two writing tasks each test, allowing 20–40 minutes per task. However, research on second language (L2) English writing has not found longer task durations to significantly improve score validity or reliability. Importantly, very few studies have compared much shorter durations for writing tasks to more traditional allotments. To explore this issue, we asked adult L2-English test takers to respond to two writing prompts with either 5-minute or 20-minute time limits. Responses were then evaluated by expert human raters and an automated writing evaluation tool. Regardless of scoring method, short duration scores evidenced equally high test-retest reliability and criterion validity as long duration scores. As expected, longer task duration yielded higher scores, but regardless of duration, test takers demonstrated the entire spectrum of writing proficiency. Implications for writing assessment are discussed in relation to scoring practices and task design. • Longer writing tasks do not have higher test-retest reliability than shorter ones. • Longer writing tasks do not have higher criterion validity than shorter ones. • The impact of task duration is not mediated by scoring method (human or machine).

Since its release in late 2022, ChatGPT and subsequent generative artificial intelligence (GAI) tools have raised a wide variety of questions and concerns for the field of technical communication: How will these tools be incorporated into professional settings? How might we appropriately integrate these tools into our research and teaching? In this review, we examine research published in 2023–2024 addressing these questions ( N = 28). Overall, we find preliminary evidence that GAI tools can positively impact student writing and assessment; they also have the potential to assist with some aspects of academic and medical research and writing. However, there are concerns about their reliability and the ethical conundrums raised when they are used inappropriately or when their outputs cannot be distinguished from humans. More research is needed for evidence-based teaching and research strategies as well as policies guiding ethical use. We offer suggestions for new research avenues and methods.

Previous literature has shown that generative artificial intelligence (GAI) software, including large language model (LLM) chatbots, might contribute to qualitative research studies. However, there is still a need to examine the relationships between researchers, GAI technologies, data, and findings. To address this need, our team conducted a thematic analysis of our reflexive journals from an LLM chatbot-assisted research project. We identified four roles that researchers adopted: managers closely monitored the LLM's work, teachers instructed the LLM on theories and methods, colleagues openly discussed the data with the LLM, and advocates worked with the LLM to improve user experiences. Planning for and playing with multiple roles also helped to enrich the research process. This study underscores the potential for using conversational roles as a framework to support reflexivity when working with GAI technologies on qualitative research.

This introductory article examines the evolving landscape of generative artificial intelligence (GAI) tools, contextualizing their impact through historical tropes of automation as both helper and threat. The authors argue that GAI tools are neither sentient helpers nor existential threats but complex systems that require careful integration into educational and research settings. The article underscores the importance of nuanced, evidence-based approaches, advocating for a balanced understanding of GAI's potential and limitations. It emphasizes ethical considerations and promotes reflective adoption over reactionary measures.

The use of generative artificial intelligence (GAI) large language models has increased in both professional and classroom technical writing settings. One common response to student use of GAI is to increase surveillance, incorporating plagiarism detection services or banning certain composing activities from the classroom. This paper argues such measures are harmful and instead proposes a “CARE” framework: critical, authorial, rhetorical, and educational—a nuanced approach emphasizing ethical and contextual AI use in technical writing classrooms. This framework aligns with plagiarism best practices, initially devised from when rhetoric and composition scholars considered the pedagogical implications of the Internet.

This study explores how prompting techniques, especially those integrated with rhetorical analysis results, may improve the effectiveness of artificial intelligence (AI)-generated business communication messages. I conducted an experiment to assess the effectiveness of these prompting techniques in the context of crafting a negative message generated with ChatGPT 3.5 ( n = 85). A multiple regression was calculated to explore prompting techniques’ impact on the negative message grades and how each technique influences the message grade. The results ( F(4, 80) = 31.84, p < .001), with an adjusted R2 = .595, indicate a positive relationship between prompting techniques and the effectiveness of AI-generated messages. This study also identified challenges related to students’ AI literacy. I conclude the study by recommending practical measures on how to incorporate AI into business and professional writing classrooms.

ChatGPT and other LLMs are at the forefront of pedagogical considerations in classrooms across the academy. Many studies have spoken to the technology’s capacity to generate one-off texts in a variety of genres. This study complements those by inquiring into its capacity to generate compelling texts at scale. In this study, we quantitatively and qualitatively analyze a small corpus of generated texts in two genres and gauge it against novice and published academic writers along known dimensions of linguistic variation. Theoretically, we position and historicize ChatGPT as a writing technology and consider the ways in which generated text may not be congruent with established trajectories of writing development in higher education. Our study found that generated texts are more informationally dense than authored texts and often read as dialogically closed, “empty,” and “fluffy.” We close with a discussion of potentially explanatory linguistic features, as well as relevant pedagogical implications.

ABSTRACT The growing capabilities of large language models (LLMs) pose important questions for rhetorical theory and pedagogy. This article offers an overview of how LLMs like GPT work and a consideration of whether they should be considered rhetorical agents. To answer this question, the article considers structural and argumentative similarities in classical theorizations of rhetoric and the philosophy of Wilfrid Sellars. GPT’s particular method of encoding statistical patterns in language gives it some rudimentary semantics and reliably generates acceptable natural language output, so it should be considered to have a degree of rhetorical agency. But it is also badly limited by its restriction to written text, and an analysis of its interface shows that much of its rhetorical savvy is caused by the highly restricted rhetorical situation created by the ChatGPT interface.

ABSTRACT Despite seemingly broad acceptance within rhetorical theory, the category of the unconscious has remained understudied and misunderstood ever since Kenneth Burke first appropriated the concept from psychoanalysis, and his unquestioned commitment to conventional anthropocentric binaries continues to obscure the role and function of the unconscious within communication into this century. Offering a corrective reanalysis of the Freudian apparatus for contemporary rhetoricians, this article shows where Burke went wrong in his early encounter with psychoanalysis and suggests a vital alternative approach in the cybernetic recasting of Jacques Lacan, which suggests the possibility of an unconscious without Dramatism’s traditional humanist assumptions. In a lateral turn bringing this imagined dialogue between Burke and Lacan into our era, the article demonstrates how a Lacan-inflected posthumanist revision of rhetoric’s unconscious is better suited to address contemporary issues of mediated communication, such as the pedagogical import of AI and ChatGPT.

This study explores how confidence levels in user prompts affect AI-generated resume text. Using six varied prompts for AI models ChatGPT-3.5, Gemini, and Perplexity, it examines how AI interprets and responds to different confidence levels. The findings reveal significant differences in AI-generated resumes based on prompt confidence, highlighting the need to adapt resume pedagogy for the AI age. Emphasizing the importance of teaching genre conventions and developing critical AI literacies, the study offers practical recommendations for integrating AI tools into resume writing instruction to better prepare students for an increasingly digital world.

The emergence of generative Artificial Intelligence (GenAI) tools such as ChatGPT has attracted wide attention in the field of L2 writing and academic writing, but few papers to date have analysed GenAI’s potential application (positive and negative) in source use practices in academic writing. This article discusses three key aspects of source use – academic attribution, searching and reading sources, and source integration. AI tools are trialled for each aspect, followed by an overall SWOT analysis. While writers can use AI tools to assist on several source use practices, they are not recommended to use AI without a deep understanding of academic writing and source use principles. This article concludes with suggestions for student writers, academic support providers, and institutions.

The first time I recall encountering artificial intelligence was in the early 2000s while working in a recording studio. After singing a take of a song, I watched as an engineer opened a plug-in called Auto-tune and then listened as he worked on tuning my vocals. I recall him explaining to me that the vocal had to be pretty close to the note I was trying to sing, otherwise the tuned version would sound fake. He demonstrated by tuning my vocal too sharp and then too flat. The sound of the stressed vocal created a distorted tremolo effect, with maybe even a bit of delay. It no longer sounded like me. It sounded like a robot who was impersonating me. I bristled. "No, not like that," I said uncomfortably laughing. "That doesn't sound like me."

Have you ever wondered how a researcher from the periphery can gain an enduring foothold in the pantheon of researchers from the center? This essay will attempt to answer that question. Halcyon Lawrence was a researcher, writer, and professor from the Global South who has made a mark on a community of technical communication scholars, writers, researchers, and professors with her widely discussed research articles dealing with the pros and cons, perils and promises, boon and bane of speech recognition tools and technology. Lawrence's research explores the thickets of speech recognition and proposes strategic and revisionary measures toward neutralizing the lopsided corpora of speech recognition software, vaporware, and artificial intelligence (AI)-powered technology. To crystalize her contributions to justice, data justice, and racial-linguistic justice, I chose a chapter, "Siri Discipline," she (2021) wrote for the book Your Computer is on Fire (Mullaney et al, 2021). My essay highlights how her ideas have gained more traction in relation to the current disruption of the AI revolution (Gopal, 2020). That disruption is often exemplified through ChatGPT, a platform that shows how Lawrence's core insight from "Siri Discipline" can have a direct bearing on normative frameworks being developed to address burgeoning challenges ushered in by the AI revolution.

In this article, we propose (re)designing privacy literacy as an essential component of our digital lives in an age of Generative Artificial Intelligence (genAI). Our study emphasizes the layered digital, technical, rhetorical, and algorithmic literacies associated with design thinking and genAI to support theorizing privacy literacy. We introduce Design as an analytical element complementary to Woods and Wason's (2021) multi-pronged framework for analyzing Terms of Service (ToS) documents. Using a cluster of Adobe Generative AI ToS, we illustrate the necessity of including Design , which allows those invested in Communication Design (CD) and Technical and Professional Communication (TPC) to interrogate how or if design supports or undermines values related to user privacy, data ownership, and informed consent. We conclude by detailing how collective surveillance apathy regarding emergent data infrastructures signal a Post-Surveillance era in our global society and digital lives.

This paper examines ChatGPT's use of evaluative language and engagement strategies while addressing information-seeking queries. It assesses the chatbot's role as a virtual teaching assistant (VTA) across various educational settings. By employing Appraisal theory, the analysis contrasts responses generated by ChatGPT and those added by humans, focusing on the interactants’ attitude, deployment of interpersonal metaphors and evaluations of entities, revealing their views on Australian cultural practice. Two datasets were analysed: the first sample (15,909 words) was retrieved from the subreddit r/AskAnAustralian and the second (10,696 words) was obtained by prompting ChatGPT with the same questions. The findings show that, while human experts mainly opt for subjective explicit formulations to express personal viewpoints, the chatbot's preference goes out to incongruent ‘it is’-constructions to share pre-programmed perspectives, which may reflect ideological bias. Even though ChatGPT displays promising socio-communicative capabilities (SCs), its lack of contextual awareness, required to function cross-culturally as a VTA, may lead to considerable ethical issues. The study's novel contribution lies in the in-depth investigation of how the chatbot's SCs and lexicogrammatical selections may impact its role as a VTA, highlighting the need to develop students’ critical digital literacy skills while using AI learning tools.

The generative AI chatbot, as an artificial rhetorical agent participating in the invention and circulation of public discourse, shakes the foundations of rhetorical tenets such as agency, ethos, circulation, and justice; and in doing so, it further isolates rhetoric as amoral, ateleological technē concerned with mere calculated effects and consequences, and may ultimately contribute to a post-rhetoric condition. This article depicts a rhetorical profile of the generative AI chatbot characterized by stochastic rhetoric, which is distinguished from the conventional understanding of rhetoric as (human) conscious and purposeful use of language to induce change. Making a case for the possibility of a post-rhetoric condition, the article considers what it might mean for our conceptualization of ethos, circulation, and justice, and suggests ways of adapting to it.

The emergence of large language models (LLMs) has disrupted approaches to writing in academic and professional contexts. While much interest has revolved around the ability of LLMs to generate coherent and generically responsible texts with minimal effort and the impact that this will have on writing careers and pedagogy, less attention has been paid to how LLMs can aid writing research. Building from previous research, this study explores the utility of AI text generators to facilitate the qualitative coding research of linguistic data. This study benchmarks five LLM prompting strategies to determine the viability of using LLMs as qualitative coding, not writing, assistants, demonstrating that LLMs can be an effective tool for classifying complex rhetorical expressions and can help business and technical communication researchers quickly produce and test their research designs, enabling them to return insights more quickly and with less initial overhead.

The ability to work with data is an important skill for students enrolled in technical and professional communication programs, but students with limited mathematical and computer programming literacies might find it difficult to do basic data analysis or customize data visualizations. This article examines the extent to which ChatGPT can make data analysis and visualization more accessible for students with limited technical proficiency. The results suggest that although the tool is poised to have a substantial impact in helping students create effective data visualizations, its efficacy as a data analysis tool is more limited.

It remains unclear if perceptions of academic dishonesty concerning artificial intelligence writing technologies (AIWTs) present new challenges or if they reflect prior, non-AI concerns. To structure this problem, we used a randomized control survey experiment. We compared student ( n = 603) and instructor ( n = 312) attitudes toward dishonesty in collaborations involving humans versus AIWT in 10 writing-related scenarios. Results suggest similar perception patterns among students and instructors, with both populations expressing significant differences in perceived dishonesty between AI and human collaborators in some scenarios. This experiment structures the problem of AI writing and academic dishonesty for future research in this emerging field.

The authors analyze the ability of ChatGPT to generate effective instructions for a consequential task: taking a COVID-19 test. They compare the output from a commercial prompt for generating these instructions to those provided by the test manufacturer. They also analyze the input, the prompt itself, to address prompt-engineering issues. The results show that although the output from ChatGPT exhibits certain conventions for documentation, the human-authored instructions from the manufacturer are superior in most ways. The authors conclude that when it comes to creating high-quality, consequential instructions, ChatGPT might be better seen as a collaborator than a competitor with human technical communicators.

How should instructors adapt technical editing courses to account for generative artificial intelligence (AI)? This article addresses what generative AI means for technical editing pedagogy. While AI tools may be able to address rote editing tasks, expert editors are still needed to provide accessible, ethical, and justice-oriented edits. After reviewing impacts of generative AI on editing praxis, the author focuses on the microcredentials that she built into an editing course in order to address these impacts pedagogically. The goal was to enable students to understand AI, argue for their expertise, and edit from ethical and social justice perspectives.

This case study offers examples of the use of artificial intelligence (AI) writing tools at a small nonprofit workplace dispute resolution center. It explores the limits and strengths of these AI tools, as well as the mediation field's concerns around using AI as a replacement for mediation work. Further, it explores the implications of AI tool use for the ethos of the writer and the AI tool itself as well as for the current pedagogy deliberations occurring in the technical writing field at large.

Artificial intelligence tools are being increasingly used to do content analysis in technical and professional communication (TPC). The authors consider some of the affordances and constraints of these tools and suggest that construct validity is an underdiscussed form of validity within TPC research that will become more important as artificial intelligence research tools become increasingly prevalent. But construct validity is an important idea for graduate programming on research methods regardless of the type of method, technique, or tool used—whether qualitative or computational. Thus, training in construct validity is important for strengthening graduate research preparation in TPC.

Preparing students to communicate in business has often been an overlooked area within literacy studies. With current calls by Cardon et al. (2024) to focus on core competencies in the business communication classroom with the emergence of generative AI, it is more important than ever to remember Berkenkotter and Huckin’s (1994) explanation that students will always learn in a form of “situated cognition embedded in disciplinary activities” (p. 3). This project, as a result, provides a framework to explore student experiences with business communication before students arrive on a college campus to better inform higher education stakeholders.

Generative AI could disrupt professional writing instruction, but banning AI tools seems unproductive. This article outlines a rhetorical approach for adapting business writing instruction for the AI age: It suggests AI use cases that align with the rhetorical canons, illustrates each with real-world business examples, and ends with suggestions for using AI to build students’ critical genre awareness. This approach should prove useful for business writing instructors who want to ground their AI-related instruction in enduring pedagogical theory.

In this discussion, we consider how the use of scenario-based assessment (SBA) can provide students with a way of developing the digital communication skills that business communication research has found they will need for the workplace, alongside other aspects of professional competence. This is because SBA can be employed to engage learners in the same types of authentic performance tasks in a situated context that they will likely encounter in their professional lives. In addition, SBA can also be used to maximize the integrity of an assignment by harnessing the positive effects of using generative Artificial Intelligence (AI) tools, while simultaneously mitigating against the misappropriation of AI by students. SBA allows learners to practice both their digital, and other, communication skills as well as contributing to their understanding of professional practice, and it also provides instructors with a powerful form of formative assessment. Our aim is to put forward a motivating and effective way of helping our students to develop the skills that they will need to become successful communicators in a postpandemic professional world.

Many experts project generative AI will impact the types of competencies that are valued among working professionals. This is the first known academic study to explore the views of business practitioners about the impacts of generative AI on skill sets. This survey of 692 business practitioners showed that business practitioners widely use generative AI, with the most common uses involving research and ideation, drafting of business messages and reports, and summarizing and revising text. Business practitioners report that character-based traits such as integrity and soft skills will become more important. Implications for teaching business communication are discussed.

In this article, we propose (re)designing privacy literacy as an essential component of our digital lives in an age of Generative Artificial Intelligence (genAI). Our study emphasizes the layered digital, technical, rhetorical, and algorithmic literacies associated with design thinking and genAI to support theorizing privacy literacy. We introduce Design as an analytical element complementary to Woods and Wason's (2021) multi-pronged framework for analyzing Terms of Service (ToS) documents. Using a cluster of Adobe Generative AI ToS, we illustrate the necessity of including Design , which allows those invested in Communication Design (CD) and Technical and Professional Communication (TPC) to interrogate how or if design supports or undermines values related to user privacy, data ownership, and informed consent. We conclude by detailing how collective surveillance apathy regarding emergent data infrastructures signal a Post-Surveillance era in our global society and digital lives.

Have you ever wondered how a researcher from the periphery can gain an enduring foothold in the pantheon of researchers from the center? This essay will attempt to answer that question. Halcyon Lawrence was a researcher, writer, and professor from the Global South who has made a mark on a community of technical communication scholars, writers, researchers, and professors with her widely discussed research articles dealing with the pros and cons, perils and promises, boon and bane of speech recognition tools and technology. Lawrence's research explores the thickets of speech recognition and proposes strategic and revisionary measures toward neutralizing the lopsided corpora of speech recognition software, vaporware, and artificial intelligence (AI)-powered technology. To crystalize her contributions to justice, data justice, and racial-linguistic justice, I chose a chapter, "Siri Discipline," she (2021) wrote for the book Your Computer is on Fire (Mullaney et al, 2021). My essay highlights how her ideas have gained more traction in relation to the current disruption of the AI revolution (Gopal, 2020). That disruption is often exemplified through ChatGPT, a platform that shows how Lawrence's core insight from "Siri Discipline" can have a direct bearing on normative frameworks being developed to address burgeoning challenges ushered in by the AI revolution.

This essay demonstrates the value of using artificial intelligence (AI) technologies to address specific kinds of research questions in rhetoric. The essay builds on a study of a novel rhetorical object first observed by Yang on the Reddit subreddit r/wallstreetbets. We demonstrate how the rhetorical structure of "pathologics" (1) generated a kind of rhetorical authority that can be measured by higher-than-average user engagement on Reddit and (2) circulated from Reddit into more traditional legacy media. Through our research on the rhetorical circulation of pathologics, we argue that researching rhetoric with AI can center new ways of knowing about concepts relevant in rhetoric, like circulation and rhetorical ecosystems. Further, we argue that researching rhetoric with AI always also entails considering a "rhetoric of AI," requiring critical attention to the platforms, infrastructures, and data sources connected to AI systems.

OpenAI's ChatGPT is a large language model (LLM) that excels at generating text and public controversy. Upon its release, many marveled at its ability to author intelligible and generically responsible texts (Herman). Writing about his students' experiences using artificial intelligence (AI) writing assistants, S. Scott Graham remarks that the results were "consistently mediocre—and usually quite obvious in their fabrication." Why might this be true? How can an LLM succeed in some respects and fail in others? We argue that the discrepant reactions to human and AI rhetoric are a question of genre, specifically that AI rhetoric is only generic; AI rhetoric represents a new enactment of "writing degree zero" (Barthes) that is disengaged from immediate rhetorical situations and knowledge bases. AI text generators (currently) have a more difficult time simulating the positioned perspectives that human writers bring to situations and communicate to audiences through their genre usage. Drawing on the work of Bakhtin, we treat this problem as a question of generic form and audience addressivity. We describe the interplay of form and addressivity as genre signaling and offer it as a construct for the analysis of AI rhetoric and genre as a cultural form (Miller). Genre signaling (Hart-Davidson and Omizo) describes a feature of communicative behavior as it occurs over time that can help both humans and machines evaluate written discourse as it exhibits certain stabilized formal features. When texts contain specific genre signals at expected frequencies and intensities, it may be recognized as being generally accurate, reliable, trustworthy. Without these signals, a text with a similar topical focus might fail to be taken as credible or useful. In this essay we propose to quantify genre signaling based on three measures: (1) stability, (2) frequency, and (3) periodicity.

The convergence of artificial intelligence technologies with the growth of Christo-fascist movements in the United States presents an alarming threat to women's health, especially considering known privacy violations by the major players—all in the shadow of the US Supreme Court's reversal of Roe v. Wade. These violations are ethotic; that is, they betray information that has been mined algorithmically to construct "user models," bits and pieces of which are sold or otherwise circulated without true "user" consent or cooperation. Such models are best understood as algorithmic ethopoeia, mathematized representations of individuals charted as matrices of commodified categories for commercial trafficking, but also for politicians and law enforcement. Taking inspiration from abolitionist tools for resisting intersectional racism, and incorporating data feminism, we offer six categories of design heuristics to respect and maintain ethopoeic integrity, especially in the domain of women's health in a post-Roe technological landscape, using a fundamental rhetorical concept to serve designers, as well as critics and activists.

Rhetoric is a trace retained in and by artificial intelligence (AI) technologies. This concept illuminates how rhetoric and AI have faced issues related to information abundance, entrenched social inequalities, discriminatory biases, and the reproduction of repressive ideologies. Drawing on their shared root terminology (stochastic/artifice), common logic (zero-agency), and similar forms of organization (trope+algorithm), this essay urges readers to consider the etymological, ontological, and formal dimensions of rhetoric as inherent features of contemporary AI.

Artificial intelligence (AI), particularly generative AI, provides a unique opportunity to reexamine how affect, memory, authenticity, embodiment, and authorship are conceptualized and discussed in rhetorical scholarship. This is particularly significant as affective experiences resulting from communication with AI are increasingly normative due to the public-facing nature of many large language model chatbots. Drawing first on a recent case wherein an AI user produced a chatbot facsimile of her childhood self, this article suggests that affective changes facilitated by AI represent not only new avenues for exploring affect, but also how time itself is experienced.

The article discusses the impact of text-generative AI in business communication pedagogy. The onset of open AI, such as ChatGPT, has the potential to transform the way faculty and students approach oral and written professional business communication. Through focus group discussions and netnography, the study employs content analysis to evaluate the strengths, weaknesses, opportunities, and threats (SWOT) of integrating AI in the teaching-learning process of business communication in a postgraduate management program. The article strives to reimagine the pedagogical tools and techniques regarding pre-reading assistance, classroom materials, assignments, evaluation, and other learning aids of business communication courses in response to the developments in text-generative AI.

Recently, formative feedback in writing instruction has been supported by technologies generally referred to as Automated Writing Evaluation tools. However, such tools are limited in their capacity to explore specific disciplinary genres, and they have shown mixed results in student writing improvement. We explore how technology-enhanced writing interventions can positively affect student attitudes toward and beliefs about writing, both reinforcing content knowledge and increasing student motivation. Using a student-facing text-visualization tool called Write & Audit, we hosted revision workshops for students (n = 30) in an introductory-level statistics course at a large North American University. The tool is designed to be flexible: instructors of various courses can create expectations and predefine topics that are genre-specific. In this way, students are offered non-evaluative formative feedback which redirects them to field-specific strategies. To gauge the usefulness of Write & Audit, we used a previously validated survey instrument designed to measure the construct model of student motivation (Ling et al. 2021). Our results show significant increases in student self-efficacy and beliefs about the importance of content in successful writing. We contextualize these findings with data from three student think-aloud interviews, which demonstrate metacognitive awareness while using the tool. Ultimately, this exploratory study is non-experimental, but it contributes a novel approach to automated formative feedback and confirms the promising potential of Write & Audit.

Abstract This article addresses a pervasive but undertheorized literacy practice: ghostwriting. Drawing on a five-year interview study with undergraduate students, I describe the many ghostwriting tasks that participants were asked to perform for their co-op jobs and how they perceived those tasks. Overall, students were bewildered by ghostwriting and found it very different from, and in some ways at odds with, their academic writing. Given the ubiquity of ghostwriting and the likelihood that much of it will be offloaded to artificial intelligence in coming years, I call for and begin to outline a critical pedagogical approach to ghostwriting grounded in critical language awareness.