This article explores four different topics of conversation in technical and professional communication (TPC) scholarship that overlap and connect with contemporary issues in generative artificial intelligence (AI): process and iteration, theory and power, actors and activity, and the social justice turn. The authors offer four nonexhaustive reviews of these conversations, offering insight into key issues and texts that have animated discourse in the field and can directly or indirectly address the complex relationship between TPC work and generative AI.

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.

This article describes graduate mentorship experiences at the Writing, Information, and Digital Experience (WIDE) research center at Michigan State University and offers a stance on graduate student mentorship. It describes WIDE’s mentorship model as feminist and inclusive and as a means to invite researchers with different backgrounds to engage in knowledge-making activities and collaborate on projects. Additionally, the article explains how WIDE enables growth for its researchers, teachers, and leaders. To illustrate these ideas, the authors provide multiple perspectives across faculty mentors, former graduate students, and current graduate students in order to discuss how WIDE researchers practice mentorship and how this mentorship prepares students for future work as scholars and researchers. Finally, the article suggests ways other research centers can adapt WIDE’s approach to their own institutional context.