All Journals

As automated writing evaluation (AWE) systems proliferate, it is important to assess them for the extent to which they serve an authentic formative assessment purpose. We developed eRevise as an AWE system to engage upper elementary students in text-based writing, receive automated feedback, and revise their essay based on that feedback. In past work, we presented a validity argument for a response-to-text formative assessment. Here, we replicate evidence for the mediational processes we identified with a second response-to-text formative assessment. Beyond replication, we expand the validity argument in several ways: We examine multiple writing outcomes to understand whether the relationships in the data generalize. We also explore patterns of relationships to better understand which students are most likely to benefit from eRevise . Furthermore, we expand the investigation from feature score improvement alone to also consider students’ conceptual development over time. Our findings provide further evidence for sociocultural mechanisms supporting students’ conceptual development of evidence use in writing. We discuss the implications of our findings for future design of eRevise and AWE systems, in general. We also discuss the need for a recursive process where our findings also contribute to refined theory development and design of future AWE systems. (1). Formative Assessment; (2) Argumentative Writing; (3) Adaptive Expertise; (4) Conceptual Change; (5) Validity Argument.

This study investigates how English as a foreign language (EFL) student reviewers engage in open-ended, dialogic interactions with generative artificial intelligence (AI) during the feedback generation process in peer assessment within EFL writing classrooms. It examines the impact of these interactions on feedback quality perceived by recipients, emotional responses (task enjoyment and anxiety), and feedback literacy. A quasi-experimental design was employed with 60 Chinese undergraduate students, divided into an experimental group (EG) that used generative AI (Doubao) for support and a control group (CG) that did not. Over three intervention cycles, data from chat histories, feedback quality ratings by recipients, and pre/post questionnaires on emotions and feedback literacy were analyzed. The results indicated that EG students primarily employed AI for linguistic refinement of their comments, with limited use for enhancing the content or structure. Nevertheless, AI support led to significant, progressive improvements in the perceived quality of feedback, particularly in affect, description, justification, and constructiveness. Furthermore, EG students reported significantly higher task enjoyment and lower anxiety compared to the CG. The intervention also positively enhanced all dimensions of feedback literacy: knowledge and abilities, willingness to participate, cooperative learning, and appreciation of peer feedback. The findings suggest that generative AI can serve as a powerful scaffold, reducing the emotional and cognitive burdens of peer assessment while fostering a more supportive and effective feedback environment. This study underscores the value of integrating AI into peer feedback practices to develop students’ feedback literacy and improve the overall quality of peer learning experiences.

Discussions of generative artificial intelligence (GenAI) focus on describing AI as either a tool or a collaborator. Such discussions do not fully grasp GenAI's transformative impact. This article proposes a model of cyborg learning that outlines the skills necessary for using GenAI effectively: background knowledge, critical evaluation of AI output, and rhetorical integration of AI output. Presenting multiple use cases from the literature in technical and professional communication and the author's own experience, the article illustrates how to write as a cyborg.

Technical editing research has examined the potential of artificial intelligence (AI) tools, yet empirical studies on their effectiveness remain limited. This quasi-experimental study investigates whether an AI-assisted tool improves copyediting performance and explores editor perceptions. The study compared 33 students’ editing performance with and without AI assistance. The results showed that an AI tool provided selective benefits, failing to raise overall correction rates but improving detection of AI-flagged errors. Perceptions varied by skill level, with stronger editors finding AI to be more distracting than helpful. The authors consider the teaching implications of this study and suggest that future research should test the effectiveness of various AI tools across different technical content types.

This case study examines how technical communication students used generative artificial intelligence (GenAI) in user-experience (UX) design via Figma. Over a semester, they engaged with Figma Jambot, AI plug-ins, and other GenAI tools. Reporting on the findings from students’ individual and team reflections, the authors explore how students used AI-assistance in UX work. Using activity theory's knotworking, the authors study how students articulate GenAI as object, tool, genre, text, and person. In functioning as a boundary object, AI helped to coordinate team workflows and translate design ideas. Then students employed rework strategies to refine designs. Finally, the authors reflect on implications for AI-assisted UX design as a cocreative process.

The release of ChatGPT in 2022 introduced both transformative opportunities and notable challenges for writing instruction and heightened instructors’ concerns about its impact on teaching and student learning. To explore how GenAI may enhance business communication in the age of AI while mitigating its negative effects on writing instruction, this Feature on Teaching article explains how instructors can proactively design custom AI tools to shift use from content generation to learning support, and offers a practical approach to adapting to an evolving, AI-driven context while exploring strategies to enhance student engagement and learning outcomes.

We explore how language development, and training informed by development, impact L2 students’ navigation of ethical collaboration with machine translation (MT). Our interdisciplinary approach integrated the Writing Studies’ concept of ‘ethical collaboration’ – an ethically guided interface between students and GenAI – with Applied Linguistics, which highlights the need to address GenAI/MT ethics and L2 language. We created a model synthesizing the “Student Guide to AI Literacy” (MLA, 2024) with a theory of L2 development. Data were collected by 1) an established protocol – direct writing in English, self-translation from the L1 and machine translation from the L1, 2) developmentally focused and acknowledgement training, and 3) post-editing of self-translation. An emergence (onset) criterion and frequencies measured written development and evaluation of MT output, frequencies measured monitoring after training and a statistical analysis measured impact of the training types. The analysis of development and evaluation demonstrated that development impacted un/ethical evaluation of MT output. The post-edits indicated that developmentally focused training encouraged ethical monitoring when development permitted and had more impact than acknowledgement training. We conclude that guidelines and training on ethical collaboration with GenAI should be informed by L2 development, not only GenAI literacy, and that research on this topic should continue.

How does writing with generative AI transform our experiences as thinkers, learners, and communicators? This study investigates the question through threshold concepts, transformative ideas that prove troublesome yet shape disciplinary participation, previously identified in writing studies through retrospective analysis. We propose that technological disruption creates conditions for observing new concepts as they emerge within communities of practice. To test this approach, we designed an experimental “AI and Writing” course. Across two iterations (Fall 2023 and Fall 2024), 38 undergraduate students from 22 majors engaged in scaffolded challenges and self-directed projects, developing strategic approaches to AI-assisted composing. Analysis of students’ final reflections, using Meyer and Land’s characteristics as sensitizing criteria, revealed three concepts essential for productive AI engagement: writing with AI is an experimental process; writing with AI requires expertise and dialogue; and writing with AI should augment rhetorical agency. These transformations prove troublesome because they contradict expectations shaped by prior experience: better tools should require less effort, linguistic fluency signals substantive knowledge, and assistance either wholly helps or harms learning and development. The findings show how AI transforms perennial questions in writing studies and composition, and they offer insights for navigating the threshold between productive collaboration and problematic dependency.

This research uses a hypothetical case discussion to make students understand writing as a thinking and managerial decision-making process versus as a technical output–based task in the context of artificial intelligence (AI)-assisted writing. Using post-and-then-pre survey data and classroom experience of students, the study reports significant improvement in students’ awareness of contextual reasoning, critical thinking, and the limitations of generative AI tools. AI’s compliance orientation and lack of human judgement was critically analysed. This article highlights the importance of sequential use of AI with humans setting the context and critically understanding the problem before giving a prompt.

The rapid integration of generative artificial intelligence (GenAI) into educational settings has created both opportunities and challenges for student learning. While GenAI adoption in education continues to grow, instructors remain divided about its educational value and appropriate usage. To further complicate matters, instructors are learning in real-time how this new and rapidly evolving tool works. Here, we describe an assignment developed for a large-enrollment, 100-level, general-education, environmental science course at the University of Arizona (RNR 150: Sustainable Earth) that provides students with structured opportunities to experiment with and critically evaluate GenAI tools. The assignment requires students to create a public facing infographic about water conservation using Piktochart's GenAI platform and then to evaluate and edit the style, format, and content of the AI output. Students then complete reflective writing analyzing the strengths and limitations of AI-generated content. Students completing this assignment in spring 2025 generally responded positively to using GenAI as a tool to support their thinking and effort, recognizing the importance of GenAI now and in their future.

Using a multiple case-study approach, this study investigated the dynamics of peer interactions that took place through Microsoft Copilot among six pairs ( n  = 12) of ESL learners, focusing on their interaction patterns, use of semiotic and multimodal resources, and functions of languaging. Our analyses illustrated that four pairs displayed collaborative orientation, one pair demonstrated an expert-novice pattern, and one pair exhibited a dominant-passive pattern. It was also found that while the collaborative group students focused more on text search and revision, the expert-novice group and the dominant-passive group allocated more time to image generation. Furthermore, in terms of languaging functions, our results revealed that all six pairs of students spent most of their time negotiating writing content. Our study has implications for technology-enhanced multimodal writing pedagogy in a generative artificial intelligence machine-in-the-loop setting.

This article explores the instructional design practices of three female artificial intelligence tech influencers (AITIs) who use YouTube to teach coding and professional literacies. Our thematic analysis of nine video transcripts featuring generative AI (GAI) yielded themes centered on influencers’ persona development, flexible instructional methods, opportunities for application, and GAI limitations. We conclude the article by identifying a utilitarian ethical dilemma wherein AITIs avoid overt feminist arguments to reach a broader audience.

As generative AI systems saturate our digital spaces with an unprecedented volume of content, aesthetics (features that elicit human cognitive and sensory responses) and taste (human discernment about the quality and desirability of those features) have drawn renewed cultural attention. For researchers in scientific and technical communication (STC), this resurgence of interest might be viewed as an invitation to revisit the aesthetic as central to the ways that technical artifacts make meaning and produce subjects.

As ChatGPT is increasingly used in second language (L2) writing practice and research, its potential to provide feedback and revision has attracted much scholarly attention. However, it remains largely unknown whether and how ChatGPT revision can influence rhetorical move-steps. This study investigates the effects of ChatGPT revision on rhetorical move-steps in English personal statements (PSs) written by L2 English undergraduate students, using a combination of corpus data and stimulated recall interviews. Based on an unstructured prompt, our analysis revealed significant reductions in the rhetorical efforts devoted to five rhetorical steps. Students’ responses highlighted both benefits and concerns regarding these revisions, illustrating how AI-generated changes can alter textual features and affect writer-reader communication from the writers’ perspective. The findings highlight the importance of students’ critical evaluation of AI-generated revisions and iterative engagement with them.

With the advent of artificial intelligence, large language model (LLM) based Automated Essay Scoring (AES) systems have been developed that can consistently make human-like decisions that do not depend fully on surface level linguistic features. However, research into the use of LLM-based AES systems is limited and little is known about the reliability, agreement, or validity of the systems. The goal of this study was to provide evidence for the reliability, agreement, and validity of LLM-based AES systems in a standardized writing assessment used for secondary school students. Both representation and generative LLM-based AES systems were developed to score persuasive essays and assessed for reliability. Then the agreement of the developed AES systems with human raters was assessed through correlational analyses. We used extrinsic convergent validation approaches to examine if the human and LLM scores correlated with linguistic components. Results indicate strong reliability and agreement for the LLM scores. In terms of convergent validity, initial correlational analyses indicated that the representation LLM AES system showed differential correlations with the human scores in terms of a text length and type-token ratio component. This result contrasts with the correlational results from the generative LLM AES model, which indicated no differences in associations between the model and human scores with regards to the linguistic components.

Automated writing evaluation (AWE) has traditionally emphasized textual features such as vocabulary and syntax, while often overlooking writers’ social identities and cognitive behaviors – factors central to understanding writing as a multidimensional construct. With the increasing integration of large language models (LLMs) into AWE, questions remain about how their assessments align with human judgments and the sources of potential divergences. This study investigates how linguistic (e.g., lexical diversity), cognitive (e.g., pausing behavior), and social (e.g., gender) factors covary with essay scores assigned by human raters and LLMs. We analyzed 4245 argumentative essays paired with demographic metadata and keystroke-logging data, using correlation analyses, random forest models, and regression-based approaches to examine relationships among writer characteristics, writing-process features, textual features, and essay scores. Results showed moderate agreement between human and LLM scores, but the two scoring systems exhibited different patterns of association with linguistic, cognitive, and social variables. These findings suggest that human and LLM evaluations rely on partially different cues and demonstrate how socio-cognitive metadata can be used to examine the factors associated with writing assessment decisions. By moving beyond text-only comparisons, this approach provides a complementary lens for understanding why and how human and machine judgments converge or diverge.

GPTZero is an AI detection platform that scans written text for statistical signatures of machine generation and returns a probability score estimating whether it was produced by a human or an AI. In higher education, many teachers have turned to AI detection as a first-line response to the integrity crisis triggered by large language models. However, empirical findings on GPTZero’s efficacy are notably mixed. Some studies report strong diagnostic value under controlled conditions, while others document substantial false-negative rates, near-random performance on certain AI-generated essays, and frequent misclassification of AI-translated texts across several languages. Multilingual and L2 writers often bear the greatest cost, as their carefully constructed English is sometimes assigned high AI-likelihood scores because their linguistic profiles may appear less natural to models trained predominantly on standard or formulaic patterns of written English. In developing countries, where students commonly write in English as a second or third language, these limitations represent more than minor technical issues; they raise concerns about equity, potentially placing disproportionate burdens on writers working to meet academic language expectations. This article argues that GPTZero is unsuitable as a definitive tool for high-stakes assessment of writing. Instead, it proposes a shift toward postplagiarism frameworks that recognize responsible AI use. Within this approach, AI detection outputs serve as formative resources for developing critical AI literacy rather than surveillance tools. Flagged content becomes a starting point for metacognitive dialogue, which supports trust-based pedagogies that emphasize student agency and intellectual accountability.

Automated essay scoring (AES) is increasingly utilized in educational settings, yet concerns about its fairness persist. This study reviews current fairness measures in AES and summarizes their respective strengths and weaknesses. Drawing on principles from educational and psychological testing, we introduce two measures for detecting potential predictive bias: conditional disparity ratio and conditional disparity difference. Our method emphasizes two key principles: first, that bias should be assessed among students with comparable proficiency levels, and second, that evaluations should be conducted on a test set independent of the AES training set. We demonstrated this approach using writing samples from the Facial Action Coding System task within the PERSUADE 2.0 corpus to assess potential predictive bias related to sex and race. Four AES models were evaluated for predictive bias: ordinal logistic regression using TF–IDF features, fine-tuned BERT, and ChatGPT in both zero-shot and few-shot settings. The findings indicated that, without accounting for proficiency, subgroup differences remained ambiguous, making it difficult to detect potential predictive bias. In contrast, conditioning on proficiency revealed clearer and more interpretable patterns of bias. The discussion addresses key factors and the extension of the bias detection framework and outlines future directions for bias mitigation. • Introduces two fairness measures for automated writing scoring. • Distinguishes predictive bias from real proficiency differences. • Uses multiple scoring models, from machine learning to large language models. • Shows fairness varies by demographic group and proficiency level. • Offers practical guidance for bias detection in AI writing assessment.

Automated Essay Scoring (AES) offers a scalable solution to the time-intensive and inconsistent nature of human scoring. While traditional AES systems require large sets of prompt-specific scored essays, large language models (LLMs) provide a powerful, adaptable alternative, capable of evaluating essays holistically without an extensive amount of pre-scored essays. However, most research on LLM-based AES focuses on resource-intensive optimization methods that are impractical for educators. In this study, we examine prompting – the most practical and accessible way for teachers to interact with LLMs – and its impact on holistic essay scoring. Using argumentative essays from secondary school students, we evaluate the effectiveness of incorporating grading rubrics, source materials, and anchor papers into prompts. Our results show that providing anchor papers significantly improves LLM-human agreement, bringing it closer to human-human scoring reliability. Moreover, while GPT-4o outperforms other models, GPT-4o mini achieves comparable results at a substantially lower cost. These findings highlight the potential of structured prompting strategies in enhancing the accuracy and accessibility of LLM-based AES in education. • Anchored prompts improve scoring reliability, nearing human-human reliability. • Rubric and exemplar prompts offer a low-resource alternative to AES. • GPT-4o is strongest; but GPT-4o mini gives similar accuracy at lower cost.

Barbara Konat offers in Emotional Appeals in Argumentation an helpful synthesis of many years of research on emotional appeals in argumentation, notably the project "Computational Pathos", supported by the Polish National Science Centre, of which she was the principal investigator.Although the title unsurprisingly echoes, given the series in which the volume appears, the terminology of argumentation theory and of so-called fallacies of "appeals to emotion", the central concern of this 160-page book, published in 2025 in the Argumentation Library series, is more specifically-and more appropriately-the rhetorical question of pathos.The volume crowns the work carried out by the group behind the "Computational Pathos" project, but it also perfectly illustrates, through its alignment with contemporary research orientations, the renewal of the field that has become visible in recent years and that rests on at least three foundations.First-and this has become self-evident, although it still struggles to penetrate certain mindsemotions should not be reduced to fallacious appeals, but should be regarded as an integral part of the means of persuasion, inseparable from reason and comparable in this respect to the construction of self-image (ethos).Second, especially when a computational dimension is introduced, analyses and models need to be grounded in texts as they actually occur, rather than in fabricated, stylized, or simplified examples.Third, due attention must be paid to the emergence of innovative methods capable of investigating fields that were once largely inaccessible.The book, however, does more than testify to the modernity of its research agenda.Across three parts, it establishes a descriptive model for the analysis of pathos, moving, as the subtitle indicates, from Aristotle to generative artificial

Rural healthcare communication networks are geographically dispersed, historically layered, and technologically uneven, sustained through evolving interpersonal and improvised ties that resist bounded frameworks like actor-network theory. This study shows how social network mapping, informed by assemblage theory, reveals coverage gaps, fragile hubs, referral chokepoints, and uneven service distribution. These loosely coupled, resource-constrained systems rely on regional patient travel, nonphysician providers, and aging infrastructure amid workforce, demographic, and funding pressures. Using concepts such as emergence and deterritorialization, we interpret these networks as adaptive yet unstable. Mapping indicates low density, centralization, weak reciprocity, and long-distance referrals, supporting artificial intelligence–enabled coordination, telehealth expansion, and improved continuity of care.

The sonic aesthetics of technical communication have been underconsidered compared to their visual and textual counterparts. We highlight the aesthetic overlaps and contrasts between human-produced audio technical communication and Generative AI audio that mimics the aesthetic patterns of successful science communication podcasts, specifically Radiolab , while exploring the sonic dimensions of ethos, relatability, trust, and narrative. Applying close listening analysis, we explore two particular sonic aesthetic features: prosodic variation and shared conversational syntax. We argue prosodic range and shared syntax contribute to the aesthetics of either overarching agreeableness/certainty or confrontation/uncertainty, which in turn constitutes an orientation to scientific knowledge.

Despite audience awareness being fundamental to writing pedagogy, student writers often struggle to move beyond considering their instructor as the primary reader, making connections between audience analysis and actual writing practice difficult. Generative artificial intelligence (GenAI) when viewed as models of culture that can be prompted to adopt various audience personas, may enable writers to interact with simulated audiences across multiple theories of audience consideration. We thus critically review a taxonomy of five audience theories: (1) writing without audiences, (2) specific people as audiences, (3) discourse communities, (4) publics, and (5) networks/algorithms. Rather than using GenAI as a text production tool, we argue for using these technologies as objects of critical inquiry that allow students to “craft and design” audiences through iterative interaction. This approach encourages writers to critique the biased design of the technology itself as well as their own biases embedded in audience descriptions.

• Generative AI can help multilingual communicators in professional writing. • Generative AI supports email/report writing and meeting summary. • Practical, ethical and legal concerns remain. • Students’ AI use at workplace informs academic writing teaching and learning. Because multilingual students’ languaging practices are not limited to academic settings, it is important to explore their lived experiences communicating in real-world situations to shed light on how to prepare them in college classrooms in the era of generative AI. Drawing upon writing samples, artifacts and interview data, this case study brings attention to the potential and challenges a multilingual international student face in implementing generative AI-assisted written communication during her 5-month internship in the workplace. The findings indicate that generative AI tools, especially ChatGPT, have the potential to help multilingual communicators meet their written linguistic demands in professional contexts, especially in email writing, report drafting and meeting summary. Generative AI-assisted writing tools could assist multilingual students with idea expression and boost their confidence and agency in communication. Yet, despite its many advantages, practical, ethical and legal concerns remain. This study contributes to the scarce yet budding literature exploring multilingual international students’ AI engagement in professional settings and offers concrete pedagogical implications and directions for future research.

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.

Despite the current widespread use of Automated Writing Evaluation (AWE) feedback, many issues regarding its efficacy still remain unresolved. Recent studies mainly focus on correctly detected errors with a lack of attention on the comprehensiveness of error detection, or error coverage. Error coverage is interesting because little is known about the capacity of AWE systems to fully detect common second language (L2) errors. It is also important to investigate the potential effect of such capacity on student uptake and retention, which are important constructs in fostering L2 writing development. To this end, the present study compared teacher feedback and AWE error coverage in L2 writing classes. The findings suggest that both the AWE system and the teacher demonstrated low error coverage across grammar, usage, and mechanics error categories. However, they indicated differences in the types of errors they identified most frequently. The AWE system flagged more mechanical errors, whereas the teacher provided twice as many corrections for grammar errors, including wrong/missing words, prepositions, and incorrect word forms. While the AWE system performed moderately in flagging articles and comma errors, it struggled with more nuanced grammatical errors, suggesting it may not be a reliable standalone tool for addressing specific needs of L2 learners’ writing challenges. Interestingly, coverage was positively associated with successful uptake, with students utilizing a wider variety of revision acts (i.e., change, add, delete, remove) on AWE errors identified compared to errors not identified. However, error coverage did not correlate with short- or long-term retention of accuracy, implying that retention may result from the interplay of error coverage with other factors. Findings provide implications for writing teachers regarding the employment of AWE systems and for AWE developers regarding the future optimizations of the AWE systems.

This article presents a curated collection of six teaching innovations presented at the Association for Business Communication 90th conference in Long Beach, California, as well as online, in October 2025. These MFA presenters demonstrated activities in helping students understand the use of artificial intelligence (AI) and social media in business communication. This My Favorite Assignment 34th edition introduces readers to a variety of classroom-ready ideas that integrate tasks involving social media and AI. Teaching support materials—instructions to students, stimulus materials, slides, rubrics, frequently asked questions, links, and sample student projects—are downloadable from the Association for Business Communication website.

Against the backdrop of rapid developments of algorithm-based feedback tools — from older tools mainly providing feedback on grammar and spelling to advanced tools based on generative artificial intelligence offering more comprehensive writing support — our meta-analysis examines to what extent algorithm-based feedback improves not only surface- (e.g., grammar and spelling) but also deep-level (e.g., structure, content, coherence) writing outcomes for different learners at secondary school and university. We reviewed experimental and quasi-experimental studies published between 2011 and the end of 2024, covering five European languages. Results from the 33 included studies indicated that algorithm-based feedback was beneficial for improving writing in general ( g = 0.36). Specifically, positive effects were observed for surface-level outcomes at posttest ( g = 0.31), though no lasting effects were found at maintenance ( g = −0.02). In contrast, deep-level writing outcomes showed sustained improvement, with positive effects both at posttest ( g = 0.31) and maintenance ( g = 0.54). No significant differences between secondary and university students were observed. However, L2 learners, in general, seemed to profit most from algorithm-based feedback, showing gains in surface- ( g = 0.77, bordering on significance), and deep-level outcomes ( g = 0.46). While no significant differences were found between the effects of specific types of algorithm-based feedback tools, feedback from Grammarly and Pigai statistically enhanced students’ writing, but effects of ChatGPT feedback were non-significant. We discuss implications for future research and educational practice, also in light of the small transfer of learning to new writing tasks. • Small effects for surface- and deep-level outcomes at posttest. • L2 learners particularly benefit. • No effect for surface-level outcomes at maintenance. • No significant difference between tools. • Small transfer effect to new writing tasks.

Abstract Drawing on Rice's concept of para-expertise, this article asks, How can we position first-year students as experts who can speak back to Large Language Models (LLM) with authority? The authors analyze essays to demonstrate how seventy-two students drew on their felt sense of a city to respond to LLM writing.

Abstract This essay posits itself against higher education's ethos of FOMO regarding Generative AI. Instead, the authors propose Move Slow and Build Community, an ethos that conceptualizes teaching as relationship-based intellectual work, and they explore how this ethos guided an interdisciplinary community of practice experimenting with Gen AI in writing courses.

Abstract This article examines the integration of large language models (LLMs) into first-year writing instruction through a creative personal narrative assignment. Drawing on student reflection data, the study explores how LLMs can support iterative revision processes, genre awareness, and voice development when introduced with limited scope and attention to process-oriented pedagogy.

Abstract Generative AI tools hold significant promise for transforming humanities classrooms into spaces of innovation, creativity, and ethical inquiry. This article presents a framework for integrating AI ethics and experimentation with generative AI platforms in English studies classrooms while applying a lens of science fiction and multimodal storytelling.

Abstract This article presents a tripartite framework — Remix, Reboot, Reframe — for supporting faculty in adapting writing pedagogy to generative AI. Grounded in reflection, choice, and play, the framework fosters critical AI literacy and invites faculty to make intentional pedagogical decisions about AI that are both responsive and innovative.

It's time for another turn.This turn, the "AI Turn," is more than simply the arrival of new technologies or the sudden acceleration of access to powerful tools -it is a complete paradigm shift, one that asks us to reconsider concepts that sit at the very heart of our disciplines: originality, authorship, creativity, literacy, imagination, composition.Nothing about this turn is minor.These concerns are at the heart of what it means to teach, to write, to understand what it means to be human.English studies is of course used to such turns.The social turn, the cultural turn, the digital turn -each brought us new questions, new answers, and new pedagogies.And yet, something about this turn feels decidedly different.Something about this turn feels world-changing in a Gutenberg-esque way.For it arrives not only with new concerns, new technologies, and new approaches to teaching and learning but also with a fundamental shift in what it means to produce and interpret text.Generative AI produces writing in ways that are increasingly so human that it begs the question of what role actual humans have in composition.Because these questions are so central to humanistic inquiry, it's no surprise that the rise of generative AI has sparked (to put it mildly) unease.At times, it can feel like these systems are encroaching on activities we have long considered distinctively human; at times, it can also feel like these systems are