As generative artificial intelligence (GenAI) tools become embedded in writing practices, researchers must refine methodologies for studying self-regulation in AI-assisted composition. While sociocognitive and co-regulation frameworks have effectively captured self-regulatory processes in human collaboration, they are insufficient for understanding how writers manage the dynamic and probabilistic nature of AI-generated text. This article introduces interplay as a methodological framework to analyze the recursive process of initiating, responding, adapting, and revising in human–AI writing interactions. Unlike co-regulation, where collaborators share communicative intent, interplay highlights the writer’s active role in interpreting and steering AI-generated content. Drawing on self-regulation theory, we propose an analytical framework that integrates traditional self-regulation categories (goal-setting, monitoring, and reflection) with interplay-specific coding (initiation, evaluation, acceptance, and adaptation). Through case analyses of human–AI writing exchanges, we demonstrate how interplay provides a systematic approach to studying agency, decision making, and regulatory strategies in AI-assisted writing. We argue that recognizing interplay as a distinct dimension of self-regulation advances both empirical research and pedagogical approaches to AI-mediated composition.

This study explores how a human-centered design (HCD) approach encourages written communication researchers to rethink methodologies when studying wicked problems, particularly in healthcare communication contexts. We argue for “methodological mutability” as a strategy to address complex and evolving challenges in rural healthcare communication. Using design thinking principles, we investigated how generative AI (GenAI) and machine learning can enhance medical communication, streamline documentation, and improve telemedicine usability. Our research revealed that rural healthcare providers view effective patient-provider communication as their primary challenge. This finding led us to pivot toward exploring how AI applications can structure and enhance patient narratives. We advocate for researchers to adopt a designer mindset, integrating methodological flexibility to move beyond problem analysis and instead develop solutions. By embedding HCD, design thinking, and methodological mutability into research design, researchers can prioritize practical interventions when working in spaces beset by wicked problems.

This State of the Inquiry (SotI) critically investigates the implications of generative artificial intelligence (GAI) for interdisciplinary research and scholarly communication within the global English-medium knowledge economy (GEMKE). Anchored in three guiding questions, the article interrogates (1) the extent to which GAI facilitates genuine interdisciplinary knowledge production versus reinforcing entrenched disciplinary silos; (2) how GAI’s dependence on established academic infrastructures influences the visibility and legitimacy of particular interdisciplinary fields; and (3) the impact of automated cross-disciplinary synthesis on the epistemic agency and intellectual labor of human scholars. While GAI holds potential to enhance research efficiency and foster new forms of interdisciplinarity, the outcomes of its integration depend largely on how scholars employ these tools; without critical and contextually informed use, it may contribute to epistemic homogenization and the marginalization of nondominant knowledge systems. The SotI advocates for a critically reflexive and contextually informed approach to the integration of GAI in academic practice, while also recognizing the capacity of scholars—particularly those on the (semi)periphery—to actively shape, adapt, and resist these tools in ways that foster inclusive and transformative interdisciplinary scholarship.

Generative artificial intelligence (GenAI) has brought into question how much ownership college students feel for “their” writing when it is AI-generated. This study recruited 88 college writers at one midwestern state university in the United States. In a within-subjects design, participants composed poems about a meaningful, challenging life experience, then prompted GenAI to compose a poem about that same event. Results showed significantly greater ownership for human-made poems; additionally, human-made poems were rated as more accurately reflective of selected lived experiences. Aesthetic merit, however, was rated higher for AI-generated poems for imagery, language, and form—but not for originality. Half the students preferred GenAI poems, mainly because of their textual features, while less than half preferred human poems, mainly for personal connections to the events presented. Implications for GenAI as a tool to support creative writing and meaningful literacy are explored.

ChatGPT has created considerable anxiety among teachers concerned that students might turn to large language models (LLMs) to write their assignments. Many of these models are able to create grammatically accurate and coherent texts, thus potentially enabling cheating and undermining literacy and critical thinking skills. This study seeks to explore the extent LLMs can mimic human-produced texts by comparing essays by ChatGPT and student writers. By analyzing 145 essays from each group, we focus on the way writers relate to their readers with respect to the positions they advance in their texts by examining the frequency and types of engagement markers. The findings reveal that student essays are significantly richer in the quantity and variety of engagement features, producing a more interactive and persuasive discourse. The ChatGPT-generated essays exhibited fewer engagement markers, particularly questions and personal asides, indicating its limitations in building interactional arguments. We attribute the patterns in ChatGPT’s output to the language data used to train the model and its underlying statistical algorithms. The study suggests a number of pedagogical implications for incorporating ChatGPT in writing instruction.

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.

The primary purpose of this study is to investigate the degree to which register knowledge, register-specific motivation, and diverse linguistic features are predictive of human judgment of writing quality in three registers—narrative, informative, and opinion. The secondary purpose is to compare the evaluation metrics of register-partitioned automated writing evaluation models in three conditions: (1) register-related factors alone, (2) linguistic features alone, and (3) the combination of these two. A total of 1006 essays ( n = 327, 342, and 337 for informative, narrative, and opinion, respectively) written by 92 fourth- and fifth-graders were examined. A series of hierarchical linear regression analyses controlling for the effects of demographics were conducted to select the most useful features to capture text quality, scored by humans, in the three registers. These features were in turn entered into automated writing evaluation predictive models with tuning of the parameters in a tenfold cross-validation procedure. The average validity coefficients (i.e., quadratic-weighed kappa, Pearson correlation r, standardized mean score difference, score deviation analysis) were computed. The results demonstrate that (1) diverse feature sets are utilized to predict quality in the three registers, and (2) the combination of register-related factors and linguistic features increases the accuracy and validity of all human and automated scoring models, especially for the registers of informative and opinion writing. The findings from this study suggest that students’ register knowledge and register-specific motivation add additional predictive information when evaluating writing quality across registers beyond that afforded by linguistic features of the paper itself, whether using human scoring or automated evaluation. These findings have practical implications for educational practitioners and scholars in that they can help strengthen consideration of register-specific writing skills and cognitive and motivational forces that are essential components of effective writing instruction and assessment.

The widespread use of irony in academic writing raises issues not considered in most psychological, linguistic, or literary approaches to irony: How is irony signalled in a written text? What are the constraints of politeness within academic discourse that govern the use and interpretation of irony? This essay considers the interpretation of one kind of irony—ironic quotation—in a controversy between linguists and artificial intelligence researchers. Irony in these published exchanges is then compared to irony in conference discussions and unpublished papers in linguistics and to irony in other disciplines. Although the analysis follows psychological and linguistic accounts of irony as echoic mention in which the same words can be reused with a different intention, it begins with the rhetorical relation of the quoting writer, the quoted writer, and the reader as members of disciplinary communities. The instances of irony that are considered both define these relations and assume them as a basis for interpretation. This analysis suggests that the study of irony can serve as a means of understanding disciplines and of examining our own taken-for-granted assumptions as academic writers.