Hi! I miss writing and sharing writing related to my higher ed work online sometimes. Here are some recent thoughts that draw on recent readings and experiences.
I continue to appreciate Eryk Salvaggio’s perspectives on AI (and not just because I was fortunate enough to work with him a while back on some Wikipedia stuff when I was at Brown!). His most recent piece picking apart agentic AI hype was particularly useful, as I currently feel a bit inundated by another wave of “AI inevitability” news articles and op-eds. His observation about the limits of “computational solutionism” may resonate with colleagues in digital humanities who have been self-reflective and cautious about their methodologies over the years:
“Not every problem is a coding problem. Access to code generation pulls us toward solving policy challenges with new lines of code, and to focus on problems that are legible to machines. This capture of the imagination of policy, industry, academia, and media habituates us to dehumanization. When thinking within the structural needs of a computer system, the blurry edge cases are no longer the soft fibers of a social fabric, but a technical nuisance. There is no simulation of a community that can solve its problems: messiness is a required step of sense-making, and of democracy.”
Similarly, Salvaggio’s comments about scale and agentic AI remind me of conversations around technological use-cases where folks gravitated towards “power tools” or methodologies that didn’t align with the smaller scope of particular projects. A new wrinkle in the agentic AI context is that users don’t often have a clear view of the resources being consumed to complete tasks or think out loud via these tools:
Agentic systems loop repeatedly, consuming far more resources than more purposefully built software. When a beginner rewrites a single line of code with an agentic system, they don’t just use one model — they activate the entire flock
I have found Salvaggio’s writing to be helpful in considering how to provide additional context to instructors and students who want more specificity in emerging and ongoing critiques of AI tools and use-cases. For example, a recent article in The Atlantic by Lila Shroff painted a rosy picture of agentic “bots that can work for hours on end, collapsing months of work into weeks, or weeks into an afternoon.”The material realities of what make agentic AI tools run for users are conveniently absent from this fantasy.
I also appreciate that Salvaggio’s critiques don’t map neatly onto what Dan Cohen calls “the two poles of Zero AI and AI For Everything.” Shroff’s piece deploys a different binary, describing “parallel universes” where we have AI slop and noise in one world and AI efficiency in another plane. The spatial language used in both contexts is interesting as someone who has spent some time thinking about mapping and visualization. Cohen’s poles resonate with me based on my experiences navigating AI discourse, though I might swap out poles for radio transmission towers or something with auditory resonances, given that I tend to think of both perspectives he cites there are different kinds of noise frequently broadcast at high volumes. Shroff’s parallel universe imagery is less compelling to me because it feels more like we inhabit a landscape increasingly encroached by slop while a crew of survivors hides out in a secure and well-stocked bunker. But maybe I am watching too much Fallout.
In other AI news, I was asked to teach a graduate-level course that focuses on AI for Salem State’s English Department this summer (part of SSU’s Graduate Certificate in Digital Studies). Here’s the student-facing course description I came up with for the course:
Topics in Digital Studies: AI
The ubiquity of artificial intelligence (AI) in our current moment invites us to reflect on relationships between digital technologies and literary studies: transformations of earlier literary objects into data, literary production in the age of augmented reality and social media, forms of close and distant reading. This course will consider recent developments in AI through the lens of digital studies, connecting emerging use-cases, critiques, and implications to earlier perspectives, projects, and methodologies taken up by digital humanities practitioners working in and around literary studies. We will also examine the roles that literature, metaphor, and rhetoric (among other areas) have played in the storytelling and speculative fictions informing AI hyperbole, marketing, reception, and resistance.
AI usage will not be required of students in any coursework, though students can make informed decisions to opt-in to particular use-cases when relevant. Students with critical perspectives of AI are welcome. Students will have the opportunity to connect course materials to their own professional, creative, and research interests.
I’m excited to develop and teach this one so I hope the description resonates with students! It will also be my first time teaching for an English department since I received my doctorate in English in 2015, which is amusing (I’ve taught courses for History and American Studies primarily). I’ve also got a graduate-level course in Digital History to prep (my second time teaching that course at SSU). It’s been super instructive (sorry) to teach and work at our Center for Teaching Innovation simultaneously.
I need to update the Teaching section of this website, I am realizing! I’ve been teaching a fair bit alongside my instructional design work started in the summer of 2020. I also need to think about how I’ll be sharing public-facing materials a bit more. I will definitely not be making more WordPress course websites; maintaining and updating them can be a bit of a pain over the years, so the ones currently documented there may end up with their records and materials looking a little different once I have some time to tweak that stuff. I’m sure you’re all on the edge of your seats!
Speaking of graduate teaching, this past week I joined some regional colleagues in the New England Faculty Development Consortium (NEFDC) for a “Community of Practice” meeting focused on graduate student support. My time at our Center for Teaching Innovation has definitely influenced my approach to graduate pedagogy; I am increasingly finding that I am adopting more strategies that I have used in undergraduate contexts: more granular lesson planning, interactive course components, assessment rubrics, assignment scaffolding.
It’s taken me some time to get more comfortable incorporating these elements into graduate course contexts; many of the graduate courses I took over the years were pretty loosely structured (perhaps a generous description) and tended to center on some combination of lecturing and class-wide discussion. I remember bristling a bit at instructors who adopted approaches that felt more like undergraduate courses, but in hindsight I also think about how much I learned from those courses. For example, I still remember an Intro to Rhetoric graduate course taught by Dr. Elizabeth Britt I took at Northeastern that included lots of peer-to-peer interaction and opportunities to revise and refine submitted writing. I did not necessarily excel at that course, but I did see my work improve over the semester and felt compelled to engage with the course materials and objectives. I’ve grown to appreciate those types of pedagogical investments over time, thankfully!
I do still need to dig more into research on graduate pedagogy as I prep my upcoming courses (and as our Center considers more targeted support for instructors teaching graduate courses). If anyone has any particular recommendations for reliable reading sources or special issues on this topic, please let me know!
Finally, here’s a list of some higher ed reads I have been checking out over the past month:
Higher Ed Reads
“AI Sycophancy Is Making Academic Bureaucracy Worse” by Sasha Perugini
My concern is not that AI introduces bureaucracy into higher education. Colleges have always been bureaucratic. My concern, rather, is that AI masks the amplification of particular management approaches as procedural improvement and institutional efficiency. It provides linguistic cover for intensified oversight, risk aversion, or conflict avoidance, making these tendencies feel evidence-based, measured, and legitimate.
“Craftivism in a Crisis: Making The Humanities Matter When It’s All Falling Apart” by Quinn Dombrowski
The ideal product of the traditional model of humanities graduate training is a scholar who goes off to the libraries and archives and reads a lot of things and and produces something that is recognizably ✨brilliant✨. And these ✨brilliant✨ scholars are put in positions where they are challenged on their ✨brilliance✨, and the successful ones learn to win these fights in ways that establish and reaffirm their place in the disciplinary intellectual hierarchy. This is why we have to teach grad students in digital humanities classes — explicitly — that our field operates under a different set of cultural norms, and the kind of lone-scholar ✨brilliance✨ ready to assertively defend its ranking in the marketplace of ideas will get you written off as someone who’s hard to collaborate with. And collaboration truly is the heart of how digital humanities works.
“(404) Job Not Found: What Workforce Training Can’t Fix for Black Atlantans in the Age of AI” by Anuli Akanegbu, PhD
Based on months-long ethnographic fieldwork in Atlanta –– a city shaped by a legacy of racial capitalism and a culture of Black self-determination –– this report interrogates what it means to be perceived as AI literate in today’s labor market, and how perceptions of skill are shaping the career outcomes of Black workers already navigating racial, spatial, occupational, and economic inequities. The findings presented in this report are situated within a broader critique of digital transformation agendas, illustrating through participant observation, in-depth interviews, and histo-cultural analysis how efforts to upskill and reskill workers often place the burden of adaptability squarely on the shoulders of individuals trying to get a foot in the door while obscuring the structural forces that restrict access to stable, high-quality employment in the first place.
“Bringing Staff Expertise Out Of The Shadows” by Aaron Stoller
Universities already employ experts in pedagogy and student development who could raise, sustain and lead critical conversations about student learning, but because their roles and work remain invisible, these conversations never happen. The question isn’t whether universities can afford to recognize academic staff expertise. It’s whether they can afford to keep erasing the expertise they’ve already hired and depend on to ensure students thrive.
Jim McGrath (he/him) is an Instructional Designer at Salem State University’s Center for Teaching Innovation. He can also be found on LinkedIn and Bluesky. Feel free to reach out if you have questions or comments!