If--and this is a big if--faculty in a college could think about the role of AI in the undergraduate curriculum as a whole, then this could be a useful response. Instead, individual faculty or (occasionally) individual departments are crafting AI policies, without making plans for the entire student experience.

There needs to be something like Godwin's Law for mentions of Elon Musk. His statements are so hyperbolic as to necessarily be strawman arguments in a piece like this.

This is framed as a challenge for the humanities, but the same could be asked about STEM research given recent advances in generative AI.

This is a refreshingly honest statement about the AI-as-calculator comparison.

Is this the fault of AI? Or is AI just shining a light on how little students are motivated to care about this work? Maybe its our curriculum and requirements and box-checking that's the problem.

I haven't heard anyone seriously claim that AI can write better books than highly trained experts.

Might there be a third category--the skills and experiences that can actually be enhanced by AI use? For example, if your goal is to help students be more critical consumers of information, then have AI give each student their own unique sample article to critique. If the goal is to have students consider a variety of potential reactions to their own writing, AI can provide hypothetical responses to a student essay from different perspectives.

I wonder if there was also discussion of faculty using AI in their work--revising lesson plans, crafting rubrics, and so on.

Given the rest of this post, I would assume that these classes involve a lot of scaffolding for students, to help them learn the process of writing essays or lab reports. If not, then that's a good place to start fighting back against the idea of product over process, with or without AI. And if that scaffolding is in place, then that provides a structure for thoughtful consideration of how AI might (or might not) support that process.

Listen to my interview with writing instructor Pary Fassihi for some examples of what this can look like: https://intentionalteaching.buzzsprout.com/2069949/episodes/14896813-ai-enhanced-learning-with-pary-fassihi.

That's true quite often, but it's not universal. It can depend on the task (e.g. this isn't writing that requires creativity, so I just need to get it started), and it can depend on the person (e.g. I freeze up at the blank page, but once I have a draft I do a ton of revision to make it what I want).

Sometimes the struggle is an undesirable difficulty, and in those cases, it's okay to get some help moving on to more desirable difficulties.

Unless we can reframe generative AI as a process-oriented tool. Instead of asking it to write a first draft, we can ask it to ask us questions to help us explore a topic. Instead of asking it for topic ideas at the outset, we can brainstorm for a while without the AI, then ask it for angles we might have missed.

I would suggest listening to my interview with Garret Westlake, who uses generative AI as part of the design thinking process he teaches: https://intentionalteaching.buzzsprout.com/2069949/episodes/13619437-design-thinking-and-ai-with-garret-westlake. He names a number of ways to weave generative AI into his students' process to help with divergent thinking and more rapid prototyping.

Agreed! The push for product over process (or credential over learning) is present at all levels of education.

The general consensus is no, these AI detectors aren't reliable enough for educators to use. See https://www.vanderbilt.edu/brightspace/2023/08/16/guidance-on-ai-detection-and-why-were-disabling-turnitins-ai-detector/ for some reasoning behind this. See also my newsletter on this: https://derekbruff.kit.com/posts/the-paradox-of-the-false-positive.

I'm curious how the author uses Turnitin's "originality detection" tools. I've seen them used very thoughtfully to focus students on the process of research and writing. Going over a Turnitin report with a student can be a useful way to help them see how to better work with sources. The report can also be used unreflectively (e.g. "This report says 34% of your paper is plagiarized, so I'm filing this with the honor council.") but it doesn't have to be.

I don't want to ignore how these tools are marketed, but having worked with educational technology for a couple of decades, it's important to remember that we don't have to use the tools in the ways they're marketed. We have agency in how we use them, and we can teach our students to have agency.

That's probably true in many cases, but I think there are policies that are developed as a response to the current and future use of generative AI in a variety of professions. Some institutions are responding to that by endeavoring to teach effective and ethical use of the tools.

I agree with all this, but I'm not sure how this is connected to being mad at generative AI. Is it because of the use of the term "intelligence" in the name?

I find that experts in an area can coach an AI to produce expert-level work, but novices in an area cannot. That has big implications for teaching novices, since we must help them develop expertise whether or not they use AI tools along the way. The really interesting question is, where can AI tools help develop that expertise?

Writing is one such laboratory. I would argue there are others, like conversation. Perhaps even conversation with an AI-powered chatbot.

"I'll know it when I see it" isn't really a criterion.

If one really wants a truly novel observation about a piece of writing, one might need to survey everything that has ever been written about that piece of writing, a task that AI might be good at.

If one is more interested in an observation that has never occurred to the current reader, then that's a different matter.

There is some debate over what kind of "creativity" generative AI has. I put that term in quotes because it clearly doesn't create like some humans do, but there are aspects of creativity or the creative process where AI can be useful. See, for instance, https://bsky.app/profile/jonippolito.bsky.social/post/3k7bymrziux2e and https://aiandacademia.substack.com/p/ai-for-futures-thinking.

I'm guessing Clune doesn't have a rubric for assessing his students' writing. "I know it when I see it" isn't particularly useful for students who are learning these skills.

Again, Clune seems to have a working idea of grade inflation and what it would mean to eliminate it, an idea that he's not articulating well here.

I would agree that some forms of this task are quite automatable, but turning data into actionable intelligence is still pretty complex work. I suspect Clune just isn't fully aware of what this work means in a field other than his own.

This is entirely sensible. Given the affordances of generative AI and its potential roles in a variety of professions, I think it's incumbent on higher education faculty to engage in this kind of discernment.

And yet he's still getting these papers (a third of all that he's read!) nearly 18 years later! As John Warner pointed out on Twitter, perhaps he should have changed his teaching practices years ago.

Clune hasn't defined grade inflation, nor has he made the case why grade inflation means "merely competent" work is getting As and Bs. He seems to be assuming his reader already knows all about grade inflation and why it's a problem.

Some, however, are calling for more substantial work from students, given that AI can handle the easy stuff. See, for instance, Ted Underwood's excellent piece on this topic: https://tedunderwood.com/2023/07/31/we-can-save-what-matters-about-writing-at-a-price/.

I've recently been polling faculty during workshops about their stance on AI this fall. There are plenty of "red lights," that is, faculty who are prohibiting students from using generative AI in their courses, but about the same number of "green lights" (allowing all kinds of AI use) and far more "yellow lights" (permitting AI with limitations).

This is a key point. If these essays aren't interesting to anyone anywhere, why do we have students write them? And, for that matter, why is it useful that ChatGPT and its peers can write them? Are there perhaps other genres of writing that are both "merely competent" (and thus in ChatGPT's wheelhouse) and also useful?

So does Clune feel some pressure to assign a high grade to work that he clearly sees as inferior? Has the system really boxed him in like this, or is he just experiencing an external locus of control?

Is problem-solving style something that can be easily deduced from early-semester surveys? Are students able to assess their own problem-solving styles through survey questions?

Upshot: How the groups work together matter more than the group composition, at least along the achievement axis.

Upshot: Don't create groups that consist entirely of low-achieving students.

This is interesting... Sometimes, the overhead costs of group work actually inhibit student learning compared to solo work.

For more on the students-as-producers approach, see this book chapter: https://ir.vanderbilt.edu/handle/1803/9446

See this blog post for a few strategies for the overbearing student problem: https://derekbruff.org/?p=4047

Here's the short answer to "How should I form student groups?" The longer answer is that there is research showing the effects of some decisions, while other choices aren't well researched.

In short, if the task is too easy or too simple, there's not a reason for students to work in groups on the task.

This paragraph is a nice high-level summary of reasons to use group work.

Co-author Cynthia Brame is all about problem solving in teaching!

This is key! The depth-first approach actually does handle the breadth. Why do you think that is?

This is key... If the things we value don't benefit from the coverage approach, there's lots more motivation to move away from that approach.

True! That's why I like the "worth being familiar with" category in the Wiggins & McTighe framework. It provides a place to put low-priority topics without dismissing them entirely.

Thanks for the concrete example! I might categorize this core concept as an "enduring understanding" in the Wiggins & McTighe framework.

I find a lot of instructors are already good with step 2, but are struggling with step 1.

I wonder if this focus on topics and concepts doesn't go far enough. If we focus on competencies (e.g. thinking like a scientist), that might do more to shift away from coverage.

To quote Angelo & Cross, "Teaching without learning is just talking."

That's a pretty big move by an influential organization, and yet... I'm not sure it's changed the biology major.

Q for the FLC: Do you ever feel this negative perception? More generally, how do your peers' views on this topic affect your teaching choices?

See also Theobald et al. (2020), https://www.pnas.org/doi/10.1073/pnas.1916903117.

I try to avoid using the term "content" because it has the connotation that the material is to be passively consumed, like a YouTube video.

There's that "continuous exposition" language from Freeman et al. (2014).

This is also key. I've known some faculty who are fairly snobbish toward their colleagues in writing and rhetoric, but the W&R folks are way ahead of other disciplines in this area. We need to learn from them.

Most of the noise these past weeks has been about AI writing generators (and early, AI image generators). But Marc is pointing to a different category here: AI research assistants. Let's not conflate the two.

GitHub Copilot is yet another category: AI coding assistants. And I imagine there are many others that educators might want to know about.

This is a very strong data / info literacy argument.

That torch has usually been carried more by librarians than faculty. I wonder if the noise around AI will motivate more faculty to incorporate info literacy into their teaching.

I need to check this out! This is the kind of targeted use of AI that has great potential for enhancing the writing process. And I can imagine other targeted uses in other disciplines. (I regularly use the Merlin app from the Cornell Lab of Ornithology to ID the birds I see.)

This is key. We need to prepare students for a professional world where these tools are used, just as we prepare engineering students for a professional world where tools like Matlab are used.

I feel that the challenges of hybrid / hyflex teaching are significant. I'm not sure the benefits are worth it. I'd rather have two sections--one in-person and one online.

I have taught in more traditional classrooms after teaching in more flexible ones. I find myself sighing a lot in the traditional classrooms.

In my experience, this is the only way "hybrid" teaching works well. You need quality cameras and microphones so the distant student can hear and see well.

Susan Hrach (author of Minding Bodies) recently tweeted about the need for instructors to have "spatial proficiency" and I think Robert is demonstrating that here. It's not enough to have a create learning space, we need to know how to navigate it with our bodies.

Don't underestimate this. There are plenty of classrooms where it's theoretically possible to move the furniture around, but practically time consuming.

Also note that there's space in the room to move things! That's not always the case

This is really interesting. An active learning space might help students work in the ways that experts in the discipline work. That's an angle on ALCs I haven't heard before.

All well and good, but IHE hasn't blocked Hypothesis!

That's likely why grading wasn't called out in the report. So many people just assume that's an unchangeable element of higher education. Why mention changing it?

That said, it seems that ungrading is having a moment. Why is that? Or is it just having a moment for me?

This is a short essay, so I don't fault Eyler for leaving this point somewhat unsupported. But suppose a faculty member pushes back on the idea that we can "ungrade" while maintaining "rigor." What would you say to that person?

I would add what's sometimes called "shadow grading," where students get a grade for their own feedback, but the course is listed as pass/fail on the transcript and for GPA calculations. MIT does this for first-semester students to ease them into the rigors of college. https://registrar.mit.edu/classes-grades-evaluations/grades/grading-policies/first-year-grading

I like Eyler's approach here, leading with research studies. I've heard some ungrading advocates lead with their own personal stories about the damaging effects grades had on their motivation or learning. Those stories don't resonate for me--grades were often very positive motivators for my education--and personal anecdotes shouldn't matter anyway. Most of our students aren't like us, so our personal experiences with grades aren't that relevant to our teaching.

Wow. I have to admit, this is a little hard to relate to. But I'm a grown adult now with a lot more perspective on grades. And back in high school, I got very good grades, so grading didn't cause me that much anxiety.

This is such a great title.

Just earlier this week, I heard the Dean of the First-Year Commons here mention that perhaps we should help students avoid taking particular combinations of courses that are known to have incredibly high workloads.

I'm glad our previous chancellor started a push for more resources for mental health and student well being a few years ago. The challenge isn't gone, but our campus has more resources for it than it used to.

What verbs do you use to describe student assignments? Do you think these words really matter?

This is a really powerful statement. How much do we know our own students' motivations and commitments? (How much does Stommel actually know his students?)

This link is dead, but the Wayback Machine tells me this syllabus is for a documentary film course (featuring both creation and critique of films). Is ungrading a lot easier in a small course in the arts and humanities than in other settings? I'm thinking, for instance, of a large-enrollment, required chemistry course.

I know faculty who are all in on "incessant surveillance" as a way to maintain the integrity of their grading. These tend to be faculty who see themselves as gatekeepers for their disciplines and their courses as weed-out courses. How do we reach faculty at this end of the spectrum?

Might blind grading be a useful approach to counteracting bias in some circumstances? Do I need to account for my students' "specific challenges" when I'm grading one of their mastery quizzes in my mathematics course? (These are quizzes that students get to retake until they pass.)

Kohn has been on-record as anti-grades for decades, typically leaning on the research around intrinsic and extrinsic motivation. Where I can't quite square the circle is that I often see my students thinking deeply, taking risks, and showing an interest in learning. Am I just recalling the exceptional students? Or are there contextual variables that need to be considered for sweeping statements like Kohn's?

So is the flipped classroom and critical race theory. Recent isn't necessarily problematic. (Cathy Davidson often makes a similar argument, noting that letter grades come from the evaluation of meat quality, but the origin of an invention doesn't necessarily determine its current usefulness.)

Here's where the nuance starts. These are all good and important questions to ask.

This is true of grading, as well.

As usual, Stommel starts with a provocative generalization that lacks nuance. (The nuance will come later.)

Having tested out a classroom last Friday, I can confirm that classwide discussion will be challenging.

Hey, that's me.

I'm really hungry.

When I plan a cryptography-themed escape room for my students, I list it as "Flex Day" in the syllabus so they don't know what's coming. One semester, the students took a group selfie for me, all flexing their biceps after they solved the clues.