Computers and Composition

This case study examines factors that significantly affect student success in online first- and second-year composition at an open-access community college and, in doing so, contributes to scholarship on online composition and student success by empirically linking community and communication through high-context communication and students’ holistic contexts to indicators of academic outcomes and validating experiences. The study analyzes 175 participant responses to five low-stakes check-in assignments designed around the course-level concept of servingness. Eighty-one patterns identified in earlier research were grouped into 14 factors from which variables were derived. These, combined with check-in responses, course grades, course, sex, ethnicity, and race, yielded a total of 27 variables for quantitative analysis. Using chi-square tests (alpha = .10) and multiple correspondence analysis, 15 of 27 variables showed significant associations with course grades. Positive associations with academic growth, peer interaction, instructor engagement, inclusion, course design, and college resources aligned with higher check-in completion and higher grades; negative associations aligned with lower check-in completion and lower grades. Background characteristics, specifically course (English 1010/2010), ethnicity (Hispanic/Non-Hispanic), and race (White/Non-White), also showed significant associations with course grades. Results suggest that academic outcomes and validating experiences must be evaluated together to inform strategies that support both academic achievement and students’ racial and cultural ways of knowing. Future research should examine how targeted, low-stakes assignments and high-context communication practices operate across institutions and student groups, including longitudinal analyses that track how community, communication, and access to college resources interact with student success over time.

This study examines how design thinking (DT) can function as a conceptual and analytical framework for supporting students’ critical AI literacy practices in writing and technical communication classrooms. Drawing on focus group interviews, individual reflection essays, and design artifacts from undergraduate technical writing students who collaboratively designed and customized AI chatbots using Poe, the study explores how students enacted responsible and user-centered design practices through each stage of the DT process. Findings reveal that 1) the empathize and define stages helped students identify algorithmic biases and frame inclusivity as part of user-centered design; 2) the ideate and prototype stages translated critical awareness into design decisions around prompt engineering, knowledge base curation, and visual accessibility; and 3) the test stage allowed students to revise their chatbot design through iterative usability testing and user feedback. Collectively, the DT process positioned students as human-in-the-loop co-creators who exercised rhetorical agency through prompt design, knowledge curation, and bias mitigation while designing customized chatbots. The article ends with pedagogical implications for integrating DT into critical AI pedagogy, chatbot design, and composition instruction.

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.

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.

The story of digital publishing in Writing Studies is one of innovation, collaboration, and do-it-yourself spirit. The field's digital publication venues emerged alongside the birth of the World Wide Web, and scholars used those venues to experiment with the possibilities of publishing in digital spaces. Visionary editors built journals with just a university server and a call for papers, and that creative spirit expanded the form and possibilities of scholarly communication. This article extends that work through the concept of “reader-choice publishing,” an approach that privileges reader needs and preferences by distributing scholarly texts in multiple open formats: HTML, PDF, and EPUB. Through a reader-choice approach, writers and publishers ask, “How will the reader use this text?” “What affordances do they need?” “What tradeoffs will they accept, and how might a single text be offered in multiple ways to offset those tradeoffs as the reader's needs and contexts change?” This article situates the reader-choice approach alongside a history of digital publishing in the field, acknowledging the past while pointing to a more usable future.

• Year 7 students can learn to code engaging animated narratives with basic Scratch. • English teachers can learn to sufficient coding to support students coding stories. • Student animated narratives of 2 – 3 min can meet English curricula requirements. • Student multimodality use can be evaluated using a criterion-based framework. • Student coding proficiency can be extended through coding animated narratives. Coding animated stories in the English classroom has been advocated from over a decade ago as an integrated curriculum context for early teaching of computer programming while simultaneously developing students’ multimodal narrative authoring. However, related research has not adequately addressed English curriculum requirements for narrative creation. This article describes the development of a framework for analysing coded animated stories from the perspective of English curriculum expectations. Analysis of 23 stories showed substantial variation in the emphasis given to different multimodal resources among those stories with the most extensive use of such resources. Stories with limited use of these resources excluded those expressing characters’ emotions and positioning the audience to experience the story from a variety of points of view. Stories with extensive multimodal expression were at the upper, but not necessarily highest, coding proficiency levels, while some with high coding proficiency showed limited use of multimodal resources. Implications are drawn for coding as an engaging creative tool in English classrooms.

• Students reported better understanding of writing with screencast assignments. • Students reported technology gains from screencast and screen recording assignments. • Students reported screencast and screen recording assignments were not complicated. • Blending spontaneous speech with the writing process helped students. • Students may feel self conscious when recording their screens and voices. Inexperienced writers often resist meaningful revision, which underscores the need for pedagogical approaches that foster deeper engagement. This study explores the use of student-led screen recordings and screencasts as pedagogical tools to promote students’ ownership and confidence in their writing processes. Our study surveyed 76 student writers in First-Year Writing classrooms to investigate this approach. The findings suggest that these assignments are easy to use, focus writers’ attention on the writing process, and leverage learning opportunities afforded by the transmodal blends of writing, video, and speech. Specifically, students reported more benefits from screencast assignments that allowed them to blend spontaneous speech into the writing process. Additionally, students reported that their technology skills improved after completing either the screencast or screen recording assignment. One downside was that students tended to feel self-conscious when recording their screens and voices. Overall, these student-led assignments are worth exploring in composition classrooms as they can lead to a deeper, more hands-on understanding of the writing process.

• 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.

In place of (or as a complement to) “user experience research,” we propose “reader experience research” as a technique for tracing how contemporary readers make meaning through a host of social-semiotic modes. Significantly, such modes are always already conditioned by cognitive, social, economic, and technological factors. To illustrate how reader experience research can account for such factors, we describe the emergence of our institution’s Reader Experience Lab. We illustrate through three experiences how the lab (and reader experience research, in general) offers opportunities for gaining insight into how contemporary readers make meaning in and/or despite what Dan Keller terms a “culture of acceleration.”

People working at the intersection of composition and user experience often serve as the connective material that binds content to use. In merging fundamental skills of both in multimodal UX, practitioners position themselves as essential mediators connecting technical information, storytelling, and technologies that carry impactful messages across disciplines, audiences, and contexts. Building on previous work that advocates for the power of narrative in AR/VR storytelling, we demonstrate how combining the composing strategy of narrative layering with user testing can guide the creation of inclusive, community-centered VR experiences. To illustrate the power of this capacity, we ground our analysis in the design of a Virtual Reality experience about advanced water purification, outlining a method for how narrative layering and UX testing can be woven together to address a variety of perspectives through interdisciplinary, layered storytelling. In doing so, we argue that multimodal UX is most powerful when it blends the needs of a range of audiences to build stories that communicate complex information in an inclusive and engaging way.

• Postgraduates engaged behaviorally, affectively, and cognitively with GenAI feedback. • Postgraduates dealt with ChatGPT primarily as a tool for refining their proposals, not for generating content. • Postgraduates demonstrated agency by actively questioning, annotating, and negotiating feedback. • Postgraduates engaged in diverse affective responses, ranging from appreciation to frustration. As Generative artificial intelligence (GenAI) tools such as ChatGPT are becoming increasingly integrated into English as a Foreign Language (EFL) academic writing context, learners’ engagement with AI-generated feedback remains insufficiently examined. This case study investigated how four Chinese EFL postgraduates joining a course in a Malaysian university engaged with ChatGPT feedback while revising their academic research proposals. The study triangulated screen recordings, pre- and post-revision drafts, and stimulated recall interviews. Participants displayed a range of behavioural strategies, including accepting, questioning, rejecting suggestions, annotating visually, and seeking external validation. Affective responses ranged from appreciation and curiosity to doubt and frustration, particularly when feedback appeared conflicting or imprecise. Cognitively, learners applied various strategies such as evaluating, comparing, negotiating feedback, and regulating its use. Yet, they showed differing levels of engagement, shaped by individual perceptions and writing intentions. Importantly, participants regarded ChatGPT as a tool for linguistic refinement rather than content generation. Overall, the findings revealed that learners did not passively receive feedback but interacted with it in agentive and critical ways. The study highlights the interplay among these three dimensions of engagement and the importance of individual differences when evaluating the pedagogical potential of GenAI-generated feedback in academic writing.

User experience (UX) as both a vocation and a skillset is currently in the center of a wicked knot: emerging technologies such as generative artificial intelligence (GenAI) and large language models (LLMs) are (for the moment) widely accessible in unprecedented ways and are already heavily integrated into modern workplace practices and educational spaces. Further, workplace demands have led to a change in perception of the function and value of UX, and the field is facing new obstacles to hiring and research funding. Our article argues that a resituation of UX is needed: we-as instructors and administrators-need to focus on UX as an act of slow, embodied, and multimodal UX composition. To do this work, we offer the strategy of détournement as central to UX curriculum and preparing students for design work in a variety of rhetorical situations, expressed through our example assignments for instructors to implement within the college classroom.

Content personalization or tailoring content as per the needs of users has been a focus of technical communicators’ work since a very long time. Recently, algorithms have helped trace users’ characteristics such as devices they use, platforms they work on, local language spoken, etc. to personalize content through strategies like responsive content, automatic translation and so on. AI tools have extended algorithmic capabilities for personalization, but at the same time increased the randomness of personalized content. That is, algorithms produce different results for the same user at different times or different results for different users at the same time with the same prompt thus shifting the agency of both rhetors (or content creators) and the audience (or content users). While conventional technical communication pedagogy has focused on writing for users, and more recently on writing for algorithms which serve the users, today it is crucial to understand how technologies like AI impact knowledge consumption processes from a user experience perspective? And how can we teach content personalization and adaptive techniques in the increasingly digital spaces of audience interactions? These questions motivated our research. To follow the roles of algorithms and technical communicators closely, we analyzed three different case studies where algorithms are responsible for a high level of personalization beyond the decisions made by technical communicators. Our findings suggest that we must teach students to investigate concepts such as user personas in UX for understanding audiences, several methods of decision-making for content assets, and rhetorical ecology for a holistic view of content production to dissemination.