Take It or Leave It with Stacey Johnson, Emily Donahoe, and Lance Eaton

A couple of months ago, the American Birding Podcast tried out a new format that host Nate Swick called “Take It or Leave It.” Nate put together a list of hot takes from the birdwatching community, noting that “birders are full of strong opinions, some serious and some silly.” He invited on the podcast two experienced birders to discuss those hot takes and, for each one, either take it (that is, agree with the hot take) or leave it (that is, disagree).

I thought the same format would work for exploring current issues in the higher education community, so I talked a few colleagues into giving it a try on this week's episode of Intentional Teaching. I spent some quality with recent essays published in the Chronicle and Insider Higher Ed and elsewhere looking for arguments about teaching and learning that would be open to debate. For each hot take, I asked our three panelists to take it or leave it, forcing them into an artificial binary that led to some robust discussion.

Who did I corral into this fun experiment? Stacey M. Johnson is director of learning and engagement at the Coalition of Urban and Metropolitan University (and a former Vanderbilt colleague of mine), Emily Pitts Donahoe is a current colleague of mine at the University of Mississippi Center for Excellence in Teaching and Learning, where she serves as an associate director of instructional support. Lance Eaton is not someone I’ve worked with, but I’ve following his work at College Unbound, a nontraditional college for nontraditional students, where he is director of faculty development.

The panelists discussed hot takes about attendance policies, "studenting" skills, and AI's role in teaching and learning. I was really happy with how the panel discussion turned out, and I'm very grateful to Stacey, Emily, and Lance for going out on a limb with me to try this format. I've already gotten a lot of positive feedback on the episode, so I'll plan on doing another one in a few months! Feel free to send me your higher ed hot takes between now and then.

​You can listen to (or read the transcript of) the "Take It or Leave It" panel here, or you can search for "Intentional Teaching" in your favorite podcast app.

Feedback and Error Correction

During the panel's discussion of generative AI's role in teaching, all three panelists had a lot to say about the notion that instructors might use AI to handle the "routine" tasks of teaching so as to free up time for more important, relational tasks, an argument put forward in this essay by Jose Antonio Bowen and C. Edward Watson. Emily listed a few of the "routine" tasks mentioned in that essay and argued that they were, in fact, relational tasks:

This led to a discussion of the use of generative AI to give students feedback on their work. Might students use AI to get some useful feedback on their writing before they turn it in for feedback from an instructor? I noted, echoing Marc Watkins in a previous podcast episode, that AI will always find something to improve in a piece of writing if you ask it to, and will do so endlessly in response to revision after revision.

At that point in the conversation, Stacey introduced a term often used in language instruction: error correction. She said that there's very little evidence that error correction of the kind an AI would likely provide has any long-term impact on student learning. Error correction can produce short-term behavior change, but hasn't been shown to be useful over longer time periods. Moreover, error correction can also lower students' confidence in learning, making it particularly problematic as an instructional method.

Like many faculty, I see error correction as an important tool in my instructional toolbox, so after the podcast recording, I reached out to Stacey to find out more about the research on error correction she mentioned. Stacey then remembered she had an almost-done blog post on that topic from months ago, so she polished up that draft and published it! It's a fascinating read and useful for both language instructors and others. See her new post, "Feedback, Error Correction, and Language Teaching," for a deep dive into error correction and other forms of feedback.

This Summer in AI Consternation

Last summer there seemed to be two key questions occupying the minds of faculty and other instructors as they pondered the impact of generative AI on teaching learning.

Answering the first question led to a series of resources from me and others on the idea of "assignment makeovers" in an age of AI, while the second question led to many institutions rolling out suggested syllabus statements on AI, like these from the University of Mississippi.

What are the big questions facing the higher education teaching community this summer as we prepare for fall classes in a world awash with AI? Based on some recent conversations and workshops I've been involved in, I'd like to offer these two candidates:

I might even expand that last question to consider the integration of AI literacy into whole programs, not just courses, but I suspect that very few departments or schools are tackling that challenge right now.

What do you think? Are questions 3 and 4 as I've framed them top of mind for you and your faculty colleagues this summer? Do you have any ideas or resources for answering these questions?

Thanks for reading!

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