TLC’s partners with the University Writing Council to share pedagogical strategies for teaching writing. Today’s author, Justin Rademaekers is WCU’s Writing Across the Curriculum Director. This is the second in a series of posts addressing writing-intensive classes in the age of generative artificial intelligence.
In Writing, AI, and General Education Goals for Our Students, I discussed the complications for teaching writing in higher education within the framework of our common general education goals to teach effective communication and critical and analytical thinking. In this post, I’ll discuss some new practical considerations.
Every year, I teach a general education course at WCU that introduces students to the fundamentals of research writing. One of the earliest assignments we do is a primary research project that asks students to develop a research question and consider how they can answer it with original primary research rather than secondary research through source material. Many students design a survey or questionnaire, others conduct interviews, and a rare student occasionally designs public observational research methods. It’s an opportunity to discuss some of the basics of research methods and data analysis and interpretation, including qualitative and quantitative.
This semester, I found myself showing students how to use grounded and thematic analysis to interpret written answers submitted by research participants. We had class debates about which responses fit into which thematic categories we were coding and highlighting with color codes. This kind of analysis with students takes time. It’s messy and imperfect. It can be fun. It can lead to interesting class conversations; and, while no student ever brought it up, the urge was there for us all to ask the much-avoided question: couldn’t we just put these written answers into an AI tool and ask for a summary of themes? Wouldn’t that be easier than our messy manual analysis during class time? I found myself reflecting on my pedagogy: is it my job now to show students how an AI tool can do this or is it my job to teach them the fundamental and traditional way of doing qualitative analysis?
I suspect that all of us have had these encounters lately and have asked ourselves similar questions. I know many colleagues who would say it is our job to teach the traditional way and to show the value of this hands-on analysis and the thinking and analytical skills it helps develop. I also know many colleagues who would say it is our job to introduce these new tools, and we’re doing a disservice to our students’ future readiness if we don’t. As a pluralist, I’m inclined to think that everyone is right.
So what’s the practical way forward with AI in the writing classroom? One of the best resources I’ve found for thinking about AI in writing classrooms is the recently published Association for Writing Across the Curriculum’s Statement on AI and Writing Across the Curriculum (Disclosure: I was the Chair of this organization and organized the team that developed this resource, but I did not author or edit it thereafter). The AWAC statement offers a “Decision-Making Framework for AI Integration” (reproduced below) which advises faculty and programs take a “restrictive approach,” “critical engagement approach,” or “productive integration approach” according to course level, disciplinary context, and assessment focus.
Decision-Making Framework for AI Integration
Before implementing AI-related activities, educators should determine whether a restrictive, critical engagement, or productive integration model best aligns with their course, discipline, and institutional context. Below is a decision-making framework to guide educators in tailoring AI use to their specific teaching needs:
| CONSIDERATION | RESTRICTIVE APPROACH (MINIMAL OR NO AI USE) | CRITICAL ENGAGEMENT APPROACH (AI AS A SITE OF INQUIRY) | PRODUCTIVE INTEGRATION APPROACH (AI AS A WRITING TOOL) |
|---|---|---|---|
| Course Level | Introductory writing courses where foundational skills need to be established without AI assistance. | First-year writing, general education, and upper-level writing courses where students analyze AI’s rhetorical and ethical dimensions. | Graduate research courses, professional writing, and advanced composition where AI is integrated into research, revision, or multimodal projects. |
| Disciplinary Context | Disciplines with strict originality requirements (e.g., philosophy, creative writing, law). | Humanities and social sciences where AI’s bias, credibility, and authorship can be analyzed critically. | STEM, business, and technical writing where AI may already be industry-standard for drafting and data analysis. |
| Institutional Policy Constraints | Universities with AI restrictions or strong plagiarism policies discouraging AI use. | Institutions encouraging academic integrity discussions but leaving AI use open-ended. | Programs that encourage AI literacy and integration as a professional skill. |
| Assessment Focus | Emphasizes human-driven composition, rhetorical awareness, and traditional research skills. | Focuses on evaluating AI critically—its rhetorical, ethical, and informational accuracy. | Encourages strategic AI use for drafting, revision, or data-driven research writing. |
| Classroom AI Policy | Prohibits AI use for assignments or allows only limited use with transparency. | Requires students to evaluate AI outputs, critique AI’s role in writing, and engage in AI literacy activities. | Encourages intentional AI use, asking students to document and reflect on how AI enhances or challenges their writing process. |
With this framework in mind, I might make the case that my course could reasonably take a “restrictive approach” on the grounds that I am teaching data analysis as a foundational skill in a field that emphasizes strict originality (writing), and with an assessment focus on “traditional research skills.” I might also make the case that should my students be introduced to AI tools for data analysis in an upper-division course within a different disciplinary context, they might feel more prepared to use AI tools after having built early foundational data analysis skills in my 200-level general education course.
This post is part of a series from the University Writing Council. The next post will feature a faculty approach that illustrates productive integration of AI tools.
