Computers and Composition最新文献

Within the growing use of peer-to-peer mobile payment apps, Venmo has proven most popular among the college-aged population in the United States. “Venmo me” is a common phrase overheard among students on college campuses. Spurred by an exchange in a course focused on social media, we investigate the uses of Venmo among students on our campus. Through interviews, we examine students’ relationships to not only the transactional but also the rhetorical aspects of Venmo, especially the social feed (unique among peer-to-peer mobile payment apps). We reflect on three recurring themes in these interviews using the concept of public displays of belongingness: insider versus outsider, requesting payment, and digital intimacy.

We share our experiences working with large-language model generative AI for a full semester in a professional writing course, integrating it into all projects. We discuss how we adapted our teaching, learning, and writing to using (or purposefully not using) AI. Issues we discuss include balancing integration of AI to avoid potential overreliance, the importance of centering authorial agency and decision-making, negotiating grading and evaluation, the benefits and drawbacks of AI throughout the writing process, and the relationships we build or could build with AI. We close with recommendations for faculty and students.

Our First-year Writing program began intentional student engagements with generative AI in the fall of 2022. We developed assignments for brainstorming research questions, writing counterarguments, and editing assistance using the AI tools Elicit, Fermat, and Wordtune. Students felt that the tools were helpful for finding ideas to get started with writing, to find sources once they had started writing, and to get help with counterarguments and alternate word choices. But when given the choice to use the assistants or not, most declined. Generative AI at this stage is unreliable, and many students found the tradeoff in reviewing AI suggestions to be too time consuming. And many students expressed a preference for continuing to develop their own voices through writing. Our experience in engaging AI led to the creation of the DEER praxis, which emphasizes defined engagements with AI tools for specific purposes, and generous use of reflection.

Research approaches that emphasize embodied practice and value the idiosyncratic uptake of writing technologies should be central to how writing researchers process the early moment of generative AI's availability to public audiences. Based on a qualitative analysis of 35 publicly available videos depicting the use of ChatGPT and Bing, the study offers a framework of descriptive codes that identify practices early adopters enact when integrating these technologies into research writing processes. The research contributes three key categories of practice that describe research writers’ interaction with generative AI across research design, writing research genres, and proofreading and editing: requesting, evaluating, and refining. This study is significant for providing an early descriptive analysis of the uptake of ChatGPT in research writing, while also identifying how disparate uses of generative AI technologies emerge from conflicting beliefs about writing, research, and invention. In particular, the study describes how experts in writing and research portray uses of and attitudes toward these technologies that often differ from students who are learning to research and write in their respective fields.

Ethical frameworks for text generators (TGs) in education are generally concerned with personalized instruction, a dependency on data, biases in training data, academic integrity, and lack of creativity from students. While broad-level, institutional guidelines provide value in understanding the ethical dimensions of artificial intelligence (AI) for the classroom, there is a need for a more ecological understanding of how AI ethics might be constructed locally, one that takes into account the negotiation of AI between teacher and student. This article investigates how an educational ethical framework for AI use emerges through a qualitative case study of one composition student's interaction with and understanding of using ChatGPT as a type of writing partner. Analysis of interview data and student logs uncover what we term an emergent “local ethic” – a framework that is capable of exploring unique ethical considerations, values, and norms that develop at the most foundational unit of higher education – the individual classroom. Our framework is meant to provide a heuristic for other writing teacher-scholars as they interrogate issues related to pedagogy, student criticality, agency, reliability, and access within the context of powerful AI systems.

This article offers Rhetorical Load Sharing as a theoretical framework for placing texts on a collaborative authorship spectrum spanning from human-authored text to synthetic text. It poses human-in-the-loop writing as a baseline ethical AI collaborative writing workflow that avoids offloading the entire rhetorical load to generative AI tools and argues that machine-in-the-loop writing, in which human collaborators retain majority of the rhetorical load, is an ideal AI collaborative writing model that is suitable for the technical and professional communication classroom.

In this paper, I investigate the ways human-AI collaboration could transform writing practices including feedback and revision. By closely examining an AI-generated essay, I expose potential gaps and contradictions in the essay and prompt ChatGPT to compose more nuanced writing. In inviting a dialectical negotiation with AI through iterative prompting and collaborative writing, I illuminate dissonances between content and style in the AI-generated essay. I proffer that interacting with AI language models can encourage students to engage critically with writerly choices by identifying instances in which AI-generated texts could consider alternative ideas or diverse perspectives. Ultimately, I contribute an innovative approach to collaborative storytelling with AI.