Frameworks for integrating AI in FYW

 A colleague asked me for information about how other programs are integrating AI into their FYW courses. I don't yet know of a really good synthesis, but here are some various frameworks I'm finding:

From Harvard's Derek Bok Center for Teaching and Learning (via RIT) 
1. "Talk directly and specifically with students about how your assignments are meant to work. Our students are not, by and large, looking for opportunities to cheat or take shortcuts. The vast majority, in fact, are just as concerned to determine the ethical and responsible use of AI as are their instructors. The primary challenge posed by generative AI is not that, in making cheating easy it will, therefore, make it rampant, but rather that its utility will blur the lines for even our most scrupulous students between seeking help or brainstorming ideas, on the one hand, and soliciting an unacceptable degree of assistance, on the other.”
2. “Disaggregate process from product, and render it visible. Now more than ever, we would encourage instructors to ask students to share early stages of their research and writing, in the form of preliminary assignments like project proposals, lists of analytical questions, annotated bibliographies, brief source analysis exercises, draft introductions, etc. Asking students to share their work in progress makes it considerably harder, not to mention less appealing, for students to outsource their thinking and writing to a large language model, as it would require them to forge, convincingly, not one but multiple phases of thinking and drafting.”
3. “Create opportunities for students to reflect on/talk about their work. So long as students imagine that they are submitting their final written work to a single reader (i.e. the instructor), and that said reader will never ask them to elaborate on, defend, or recapitulate their ideas in further conversation, leaning on generative AI might seem like a relatively safe (even victimless) indiscretion. If, however, students realize that they may have many readers—and, moreover, that those readers will ask them many questions about their writing [...] the value proposition of outsourcing all of those decisions to a large language model that won’t be able to help them respond to their readers in the moment becomes much less appealing.”

Dobrin's Four Ways to Integrate AI in Writing (see pp 21-22)
--> AI for invention, AI for revision, AI for critical thinking, AI for research

An AI literacy-focused approach in an L2 setting (Hakim, 2025)
"AI literacy includes a set of skills, competencies, and practices for using GenAI tools. For language learning, it comprises five elements: understand, access, prompt, corroborate, and incorporate (Tseng and Warschauer 2023; Warschauer et al. 2023). In this framework, AI literacy entails students developing an understanding of GenAI tools' capabilities and limitations, navigation, prompt development, verification of content, incorporation of AI-generated content, and adherence to ethical citation practices."  In this approach, the author/instructor aligns major curricular activities with Tseng and Warshauer's five AI literacy elements.

From Basgier and Wilkes (2025, “From an Unsettled Middle: A Critical-Ethical Stance for GenAI-Engaged Writing Assignments,” https://doi.org/10.58680/ccc202577162)

"overreliance on GenAI is a learned behavior, not a natural or inevitable one" (p. 67)

 "following Justin Rademaekers, we use linguist Tim John Moore’s framework for critical thinking as a lens for analyzing GenAI-engaged assignments from across the curriculum, and we suggest ways that educators who refuse to use or assign GenAI can still proactively teach critical thinking about the technology in discipline-specific ways" (p. 65)

Moore's framework: students need to learn to navigate among seven dimensional pairs of critical thinking, which differ in emphasis from field to field:

• text-internal/text-external (text vs other data as focus of inquiry)
• objectivist/subjectivist (level of positionality in critical thinking)
• heuristic/hermeneutic (deductive/pre-determined vs inductive/bottom-up)
• theory-explicit/theory-implicit (level of explicit reference to theory)
• evaluative/interpretive ("meaning" with vs without value judgment)
• epistemic/deontic ("truth" vs "action" as focus of inquiry)
• neutralist/activist (political/social aim of inquiry)
• Basgier and Wilke's addition: implied ethics/explicit ethics (explicitness of the ethical stance toward the object of inquiry)

Additional Notes

Brinkman (2025) argues for "transparency" and "metacognition" as essential commitments in his effort to redesign his FYW curriculum. 

Panning Davies and Navickas's (2025) course design in Composition Studies builds around student-created data, sustained revision, and incorporation of critical AI literacies in a writing-about-writing approach.

LaGuardia CC (2025) offers an OER set of lessons designed for integrating a substantial AI unit into FYW. The AI-related content is embedded into activities with a"heavy emphasis on writing-as-process, critical reading, gathering information, inquiry-based writing, and taking research notes."