Generative artificial intelligence (AI) is reshaping technical communication, necessitating strategies to assess its impact. This article introduces a framework combining human-in-the-loop automation with a task-based approach for communication roles. Effective AI integration requires identifying and organizing key writing tasks to fit into automated workflows. The framework underscores the value of writing expertise and offers practical guidance for practitioners, scholars, and educators. By aligning AI tools with technical communication tasks, professionals can produce accurate and complex communication products. This approach highlights the essential role of human expertise in effective, AI-assisted writing.

This article proposes the Canon to Code (C2C) Auditing Framework for evaluating generative (artificial intelligence) AI output through classical rhetoric, arguing that AI's characteristic failures—guessing instead of knowing, politeness instead of credibility, and confidence instead of judgment—revisit problems that rhetoric has addressed since antiquity. Developed using a rulemaking methodology and drawing on classical rhetorical theory, this framework presents 10 auditing rules that operationalize rhetorical principles into evaluation criteria for AI-generated content, focusing on accuracy, transparency, and accountability. It offers content auditors, technical communicators, and compliance professionals a theoretically grounded method for distinguishing AI output that meets audience needs from output that simulates credibility through pattern matching.

This article proposes a sensemaking methodology to enhance human-in-the-loop technical and professional communication (TPC) practices when working with generative artificial intelligence (GenAI) output, which is often ambiguous and not always accurate. Sensemaking describes actions and cognitive strategies humans use to make sense of new/ambiguous information. We argue that sensemaking can help TPC students navigate making sense of GenAI output for better judgment in evaluating AI output. Particularly, we leverage sensemaking's Situation-Gap-Bridge-Outcome framework as a heuristic to identify situational contexts outside of GenAI, gaps in knowledge, create bridges for those gaps, and evaluate outcomes and connect this to extant TPC literature and discuss its implications.

Drawing on rhetorical genre studies, we explore research article abstracts created by generative artificial intelligence (AI). These synthetic genres—genre-ing activities shaped by the recursive nature of language learning models in AI-driven text generation—are of interest as they could influence informational quality, leading to various forms of disordered information such as misinformation. We conduct a two-part study generating abstracts about (a) genre scholarship and (b) polarized topics subject to misinformation. We conclude with considerations about this speculative domain of AI text generation and dis/misinformation spread and how genre approaches may be instructive in its identification.

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.

This article follows up on the conversation about new streams of approaches in technical communication and user experience (UX) design, i.e., design thinking, content strategy, and artificial intelligence (AI), which afford implications for professional practice. By extending such implications to technical communication pedagogy, we aim to demonstrate the importance of paying attention to these streams in our programmatic development and provide strategies for doing so.

Technical and professional communication (TPC) and user experience (UX) design are often seen as intertwined due to being user-centered. Yet, as widening industry positions combine TPC and UX, new streams enrich our understanding. This article looks at three such streams, namely, design thinking, content strategy, and artificial intelligence to uncover specific industry practices, skills, and ways to advocate for users. These streams foster a multistage user-centered methodology focused on a continuous designing process, strategic ways for developing content across different platforms and channels, and for developing in smart contexts where agentive products act for users. In this article, we synthesize these developments and draw out how these impact TPC.

The current job market favors young technical writers who are skilled in the way of the computer both as a subject of writing and as a production tool. In the technical writing classroom students can be exposed to this important technology through assignments that include computerized instruction, word processing, text analysis, artificial intelligence, and communications.

The following treatise surveys the issues and approaches for designing a computer system capable of reading, understanding, and writing technical reports. Recent progress in computer science and artificial intelligence research is used to specify the nature of the modules in the system. The processing of a sample text is observed during the phases of reading and writing a report on the origin of sunspots. The author advances some proposals for correlating syntax and semantics of English from a procedural standpoint. The discussion is illustrated with structural diagrams.

Evolving from principles observed in telecommunications and servomechanisms, cybernetics is now a broad interdisciplinary science that embraces all biological and environmental activities, including human thought and social organization. Cybernetics provides the unifying basis for various scientific techniques used in business management today, but computer-aided administration and factory automation are only a start. Eventually, comprehensive systems of artificial intelligence will function at the highest level to direct, not merely manage, the total operation of industry, even the administration of society itself. Clearly, there will be marked impact on human communication, especially in scientific and technical publications for marketing and training.