Computers and Composition最新文献

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.

This study introduces online advertising platforms as digital composing tools where persuasive rhetoric encourages users to follow links and take action on landing pages. It frames these platforms as digital spaces where human actors work alongside non-human AI agents (Duin & Pedersen, 2021 & 2023) and where rhetorical agency emerges through the activity of machine learning and artificial intelligence. It theorizes a (human) user-centered approach to composing digital ads in digital advertising platforms built around Walton, Moore & Jones’ (2019) framework of positionality, position, and power. It provides guidance for technical and professional writers in placing human users at the center of an abstracted, algorithm-driven partnership where generative AI appears poised to wrest power from both composers and users.

Compositionists can train students to rhetorically approach AI as a writing assistant—a metaphor that can help students maintain authority in dialogue with AI. Such an approach allows students to control the input and output from generative AI. Compositionists can teach students how to input ethical information and recognize harmful output. The students are then encouraged to converse with the AI in dialogue, sending messages back and forth until the AI generates writing that is acceptable in ethics and content. This article provides a social justice heuristic for compositions to follow in their training of students.