(A few) Answers on Resources, Pedagogy, and Academic Integrity
This post is the first in a series responding to feedback we received during and immediately following the July 26 webinar “What AI Means for Teaching.” We were excited to receive nearly 180 comments and questions, and while we can’t respond directly to each one, common threads emerged as we read about people’s concerns and inquiries. In this post we will respond to those around teaching resources, pedagogy, and academic integrity. In our next post we’ll examine issues of labor, policy, and department- and program-level resources.
Teaching Resources and Training
Two themes were clear in requests for teaching resources:
Make it practical. The most common words used in appeals for resources were “concrete,” “specific,” and “useable.” One respondent commented, “This is helpful, but don’t just tell me ‘Use Chat GPT to help teach outlining. Tell me what that looks like!’”
The Task Force has already started to make some sample AI lessons available in a recent post (please see first three source links from Anna Mills and Antonio Byrd) And just because we’re helpful, here’s a couple examples of outlining lessons from Sarah Z Johnson that use AI to accomplish different learning objectives related to organization.
Make it manageable. Many respondents reported feeling overwhelmed by the amount of information available. They asked that MLA and CCCC offer lesson plans organized by type so that teachers from different disciplines can browse lessons and borrow from each other. We see our diverse areas of expertise as a huge asset and loved this idea! Also, we hear that people are seeking a resource bank where choices are pared down, manageable, and updated regularly. While the members of the TF hesitate to commit to a painstakingly selected and continually current resource (see next week’s post on labor!), we are currently investigating ways to host a crowd-sourced bank with easily searchable filters.
We have heard folks in their desire for a repository of AI lesson plans, and we hope to host a space in this site for such a resource soon.
While many respondents sought more practical advice, several comments also showed colleagues grappling thoughtfully with the larger implications of how AI will change the practice of teaching writing both in and outside the humanities. Questions about the role of assessment, AI’s impact on equity work, and how to incorporate global perspectives into our discourse on AI were common themes.
Feedback encouraged us to help members keep “focused on our students’ process,” centering a “pedagogy of writing-as-learning.” And of course, because we are all developing new pedagogies around critical AI literacy, the Task Force plans to focus much future research and communication around this particular issue.
As we continue this work, the Joint Task Force will not lose sight of the important role professional organizations like MLA and CCCC play in the decisions we make as teachers and program leaders. Feedback pointed out gaps in membership on the Task Force, and we plan to include representation from modern language departments and others in future work.
In commenting about academic integrity, respondents were well aware that the common chatter around “AI cheating” in the popular discourse didn’t capture the nuance of their concerns. They recognized that humanities teachers have a long history of developing assessment strategies that encourage critical thought, and in fact, discourage the wholesale regurgitation that LLMs are so good at.
Participants also saw, though, that Generative AI presented new challenges and temptations to their students. A number of comments encouraged us to “take a clear stand,” so students, administrators, and even the general public understand that faculty who use writing as part of their praxis have a commitment to the integrity of their programs and also to preparing students for their civic and professional lives in a world saturated with Generative AI.
This Task Force will continue to keep an eye on large trends and amplify emerging concerns and opportunities in AI and writing. We welcome your ongoing feedback as we continue this work. Next week, we’ll shift our focus to consider policy, labor, and program resources.