Computers and Composition

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.

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.

Effective source use is a critical skill for first-year writing students because it prepares them for academic, professional, and civic engagement; however, existing research demonstrates that selecting appropriate sources and engaging them insightfully remains a significant challenge. While students struggle with the combined pressures to read, evaluate, and synthesize scholarly sources, we argue that online media including news articles, opinion pieces, and social media posts are a potent but underutilized resource for building students’ competence and confidence with source use. In this article, we present the methods that we have collaboratively developed as an instruction librarian and a first-year writing instructor to propose a new approach to teaching undergraduate research using online media. We detail strategies for teaching advanced search skills using Google and social media platforms like Reddit and X (formerly Twitter), as well as a “reception study” writing assignment that requires students to develop source evaluation and synthesis skills for engaging these online sources. The success of our module highlights that enabling students to build their research skills in the context of these more familiar source formats can lead them to an enriched understanding of the research process—including formulating an authentic research inquiry and engaging meaningfully with real audiences—while also building their skills in accessing, evaluating, and synthesizing diverse sources. Furthermore, by developing research skills in the context of social media platforms and online popular media sources, students gain a practical sense of the relevance of academic research skills to their daily research habits.

With respect to AI writing technologies (AIWT), we pose three foundational questions about academic dishonesty. First, do writing instructors and students perceive differences between AI agents and human agents in classroom scenarios? Second, to what extent are writing instructor and student perceptions are aligned? Third, what types of writing scenarios are perceived as academic dishonesty? Answering these questions provides a baseline of comparison not only for future studies of AIWT collaboration but also contextualizes perceptions of human-to-human collaboration. We report on a large-scale experimental survey study that answers these questions using item response theory (IRT). Our findings demonstrate that while there are differences between AI and human agents of collaborations, writing instructors and students are generally aligned in their perceptions. Using a Rasch model, we find that academic dishonesty operates along a spectrum of textual production. Regardless of whether the collaborating agent is human or AI, the more an agent produces text, the more this collaboration is perceived as academic dishonesty. Conversely, the less text that is produced, the less this scenario is perceived as academically dishonest. In our discussion, we provide a data-driven heuristic to guide instructors and administrators.

Over the past decade, compositionists have made a number of claims about opportunities presented by Wikipedia for teaching writing. The encyclopedia allows for transparent observation of concepts and skills related to process, research, collaboration, and rhetoric. Beyond observation, Wikipedia allows for public writing with an authentic audience, which often results in increased motivation. Much of this early research has dealt in opportunities and possibilities: speculation about how Wikipedia sponsors particular pedagogies and learning outcomes, and there remains a need for more empirical evidence. This article presents select data from a recent large-scale study conducted by the Wiki Education Foundation that begins to meet this need, and that confirms and extends research from the computers and writing community. Key findings from this research include positive evaluations of Wikipedia-based assignments in general, as well as positive evaluations concerning the capacity of Wikipedia-based assignments to teach critical thinking skills, source evaluation and research, public writing, literature review and synthesis, and peer review. This study also adds significantly to our field's knowledge of how contextual factors related to the course and assignment affect students’ evaluation of a Wikipedia-based assignment. Finally, this article suggests key recommendations for teaching with Wikipedia based on these findings.

In this article, we describe the philosophy, objectives, and development of Multilingual User-Experience (Multilingual UX), a community-driven initiative for supporting technology innovation with marginalized communities. We highlight how community-based mentorship can guide innovative technology design through an intersectional technofeminist perspective. We begin with a discussion of the impetus for building this initiative before discussing how we are collaboratively designing a research center to facilitate technology design with and for marginalized communities. We both theorize and enact the intersectional technofeminist principles of our work by telling the story of our project with our collaborators and community partners, in the form of vignettes from a symposium. We conclude by looking ahead to our next steps and by offering strategies for intersectional technofeminist community building and technology innovation, in the hope that our experiences can be further developed and localized to support similar initiatives that highlight the value of feminist collaboration in technology design.

The authors, an instructor and students, describe our practice of user-centered design on three levels: in the design and structure of an advanced undergraduate course in which we all participated, in student projects designed during the course, and in our reflections on the course presented here. We argue that principles of user-centered design can and should be more than course concepts and assignments; they can be core practices of the course that hold both students and teachers accountable for the impacts of their rhetorical choices. We offer a model for other teacher-scholars looking to involve students in the design of their courses and in writing together about their work.

This article explores textual curation as a conceptualization of authorship and composition within large information structures that is heavily based on the canon of arrangement. This work is often undertaken through distributed collaboration, thus complicating traditional conceptions of authorial attribution and agency. Central curatorial processes include critical recomposition of prior texts along with the development of small and often invisible textual elements such as architecture, metadata, and strategic links. I offer a grounded definition of textual curation that draws from traditional curatorial fields such as Museum Studies and Library Science as well as Writing Studies’ own subfield of Technical Communication, which focuses heavily on recomposed, collaboratively produced texts. Selected Wikipedia articles serve as case studies for examining live curatorial work in open, collaborative environments.