Since the Fall of 2022 (shortly after the public rollout of ChatGPT’s publicly-accessible GPT-3.5 version), a not-insignificant portion of my day job as an instructional designer has involved assessing, discussing, and sharing information about the impact of artificial intelligence (and especially generative AI tools like chatbots and agents) on higher ed. Most of that work has primarily been intended for local audiences here on campus, aside from a presentation on AI and digital literacies at 2023’s Massachusetts Colleges Online Conference. I’ve facilitated information sessions and faculty panels on AI, run faculty professional learning communities, led workshops, developed resources (for the university as well as for programs and individual courses), had lots of consultations and conversations (mostly with faculty and staff due to my faculty-facing role, but I have been fortunate to talk to many students too), contributed to spaces like the POD network’s AI listserv (which I recommend!), and attended dozens and dozens of webinars and professional development events. I’m currently co-facilitating an AI Advisory Group made up of a wide range of campus stakeholders and we’ll be sharing some recommendations with our campus community and with university leadership in the coming months; I will share anything relevant on this website as I shore up my instructional design content here.
There are a few reasons why I haven’t said much publicly about my work in the AI and higher ed space: it’s a contentious topic (especially on social media networks like Bluesky); it’s a topic I wanted to learn more about; it’s a topic that I’m interested in but not necessarily enamored of; in 2022 I was still relatively new (two years in) to instructional design work and unsure of what my professional future might hold; professional writing for academic publication is not something I am paid to do and is not a core lens through which I am assessed; I had other stuff going on in my life (mostly good!); I just didn’t really want to (among other reasons).
But I’ve been encouraged to amplify my perspective in this area and I do have more time to dedicate to this sort of writing, so here we are. I’ll also note that my experiences are mostly within United States higher ed contexts (though I do research more broadly beyond those borders and have valued many of the perspectives I have come across). I wanted to start this pivot to more public writing on AI and higher ed by outlining what I am most interested in right now regarding this topic.
In the AI and higher ed worlds, we see a lot of energy and discussion dedicated to the topics of “AI literacy” and “digital literacy” more broadly. If you’ve had any ties at all to higher ed over the last half-decade, you know that the onset of the global pandemic resulted in a major pivot to investments in online learning modalities, especially online synchronous (and perhaps to a lesser extent, online asynchronous) learning modalities. The conditions (primarily economic) motivating this pivot also amplified some troubling realities regarding the range of digital resources and digital literacies possessed by higher ed’s institutions. For example, while many colleges have provided instructors with learning management systems (LMSs) like Canvas and Blackboard for decades, it was clear that not all instructors were well-versed in their features and components, suggesting that their usage varied wildly depending on the institution, department, or program. New technologies like Zoom and classroom multimedia were quickly rolled out to meet new modality requirements in time for Fall 2020 semesters. Even instructors who would not self-identify as emerging technologists struggled at times with the timeline of technological adoption and course remediation, especially in course contexts where in-person interactions with course materials, instructors, and classmates seemed more ideally suited to both course objectives and instructor experiences. The need for continued employment and reliable income motivated many instructors to comply with these disruptive learning conditions, as did more general social and cultural investments in certain ideas of “normalcy” and production.
OpenAI launched ChatGPT in November 2022, its own rippling effects running right into the ripples of the two and a half years dedicated to pandemic teaching practices (depending on when or if or how you believe that we arrived at a “post-pandemic” existence in the U.S.). I was just watching a webinar earlier today that presented a tidier narrative of this shift of attention from pandemic learning to AI learning. The speaker seemed to be viewing (or at least rhetorically situating) the latter as a continuation of the digital literacy investments central to pandemic teaching. In fact, that time period felt more like escaping a sinking ship and finding some relatively sturdy wreckage to cling to, only to look out and discover that the water was infested with sharks. Faculty and student support resources are often tasked with the rescue mission. Unfortunately, we are also potentially part of the dinner menu for those same sharks; in many metaphorical respects, we are floating right next to those faculty members and similarly worried, fatigued, and frustrated.
The first iteration of our center’s resource page on “Teaching In The Age of AI” launched in the early Spring of 2023 to provide some support to instructors looking for advice on how to navigate chatbots and their emerging impact. Most of that initial advice stressed an abundance of caution and acknowledged that the form and impact of AI were still in the early stages. We encouraged folks to learn more about the topic, sharing emerging research and inventorying the more visible tools and terms that we encountered. We recommended that instructors talk directly with students about their current perspectives on AI and its relevance to coursework, course topics, and relevant professional contexts, regardless of whether those points of view were skeptical, excited, or somewhere in between. And we encouraged instructors not to use AI detectors due to the prevalence of “false positives” and the negative effects that usage may have on the student learning experience. At the same time we were evaluating AI’s relevance to our work as instructional designers and faculty support, considering our own digital literacies, consulting with regional and national colleagues, reading AI news coverage from higher ed outlets and major news media, scanning social media, and encountering some emerging examples of practitioners and tools that seemed designed to either augment or replace some of the core tasks we provided our campus community.
It was interesting to encounter the early AI adopters and advocates in higher ed during these information-gathering efforts. It was especially clear to me that some individuals saw AI’s disruption as an opportunity to improve their individual standings in particular institutions or in higher ed more generally. Not all of these sources of AI perspectives seemed to operate along these lines, even if their visibility did benefit from their emergence as trusted and informed resources. As I previously mentioned, POD’s AI listserv became (and remains) a valuable network for folks to raise questions, share perspectives, articulate emerging critiques, highlight resources. I’ll focus on my own positive experiences with a few people there and elsewhere in case you’re looking for additional points of view (and I’ll note that this is by no means an exhaustive list or a curated selection of folks who all view AI exactly the same way):
- Anna Mills and John Warner, who have primarily focused on AI’s impact on writing and writing instruction
- Maha Bali, who clearly outlines a specified approach to AI literacy while also acknowledging critiques of AI in education
- My friends from the worlds of digital humanities, who take a cautious and informed consideration of extractive and harmful tendencies in the application of AI to humanities labor
- Janelle Shane, Joy Buolamwini, and other AI researchers like the team at the Distributed AI Research Institute, who were at the forefront of this topic before ChatGPT and the ascendance of generative AI
Many of those voices and perspectives have circulated widely and visibly in higher ed’s considerations of AI. At the same time, I have noticed that some of the louder voices in higher ed on this topic tend to skew more towards AI adoption, if not “AI evangelism.” I view the AI Evangelist as a speaker who might:
- Speak sententiously about an abstracted “AI” to insist upon its unquestioned importance
- Lean into fearmongering with phrases like “AI won’t replace you, but someone who knows how to use AI will”
- Dismiss concerns about AI’s climate impact by reminding their audience that they too, consume energy doing things in modern life
- Invoke “ethics” without really providing a response to the ethical considerations that have led their peers to think more carefully and specifically about AI’s role in higher ed’s future
- Compare AI to earlier technological disruptions or innovations while ignoring the frictions, critiques, and short and long-term impact of those technologies and their initial perceptions and economic valuations (Web 2.0, social media, email, smart phones, among many others)
- Prioritize the ideals of management culture and their dreams of AI efficiency at the expense of other forms of labor
- Ignore, obscure, disavow, or dismiss their individual success (professional, economic, attention-based) at the expense of our collective benefit as laborers in higher ed
It’s these last two bullet points that I’d like to linger on for a bit longer here. To be honest, I am starting to write about this topic in large part because I think that “labor literacy” is something that educators need to talk about and think about more within the contexts of AI literacy and digital literacy. Readings of technology through the lens of labor ideally have investments in intersectionality as well and likely consider non-digital contexts. Here I am thinking of books like:
- Sasha Costanza-Chock’s Design Justice: Community-Led Practices To Build The Worlds We Need
- Caroline Crialdo Perez’s Invisible Women: Exposing Data Bias In A World Designed By Men
- Kate Losse’s The Boy Kings: A Journey Into The Heart of The Social Network
- Tara McPherson’s Feminist In A Software Lab: Difference + Design
- Brian Merchant’s Blood In The Machine: The Origins of The Rebellion Against Big Tech
To return to the more specialized terrain I invoked at the outset, I have reached a point where, moving forward, I am primarily interested in considerations of AI’s impact that reckon with questions of AI literacy, digital literacy, and labor literacy. For example, one of the talking points valued by AI Evangelists as well as well-intended faculty and staff has been the potential role that AI can play in what is increasingly (and unfortunately) called “workforce development.” The perspective on workforce development might cite recent surveys of managers and industry leaders, tout examples of labor “efficiency,” promise employees that the work they are less enthused about will one day be automated. Many of these positives are rhetorically framed as benefits to the early-career employee, the recent college graduate. Rarely, if ever, do we hear representatives from higher ed speak with such urgency about, say, the potential benefits of creating space in the curriculum to teach students how to join or start unions, review the recent history of organized labor, consider the role that data surveillance plays in employee assessment and management culture. Why are the calls for labor literacy not as loud as the calls for AI literacy in this contemporary moment? I have asked this question at a few recent AI webinars, and I can’t recall the question being acknowledged by the speakers, let alone answered.
To conclude for now, what might the intersection of AI literacy, digital literacy, and labor literacy look like in higher ed? I’m still thinking about this question. Here are a few more specific lines of inquiry:
- How do investments in AI usage impact institutional commitments to sustainability (often articulated in university strategic plans)?
- How does AI usage impact employee-created data in higher ed and its present and future uses: course materials, video lectures, LMS content, research publications, posters / presentations?
- What AI tools are approved for community usage and how are they assessed (and later reviewed and reassessed, if needed)?
- How do campus sectors with various areas of expertise (in course design, student success, accessibility, among other areas) evaluate AI’s implications on their work? What opportunities for institutional collaboration and support are available?
- How do particular investments in particular types of AI literacy impact particular types of campus labor, and what resources are made available for professional development, expanded job responsibilities, additional labor costs and needs?
- How is AI literacy viewed as an ongoing initiative and supported with relevant resources: technologies, staffing, training, etc.?
- How are the benefits of certain AI tools and use-cases disseminated to faculty and staff and what are the expectations of usage in these contexts?
- How is faculty autonomy complicated by concerns about AI: pivots to handwritten modalities of labor in classroom activities, uses of surveillance technology to assess content copied and pasted into writing interfaces, assessments of student work that claim to identify student AI usage, core digital literacy expectations of faculty and students, among other areas?
- How do institutions balance desires for speed and efficiency in digital literacy contexts with concerns about other core digital literacy needs for students, especially students who may have been negatively impacted by early pandemic conditions?
- When should classrooms and courses align more closely with relevant professional contexts and when might they productively resist or reimagine those conditions of labor in ways that align with course or program goals (as opposed to workforce goals)?
- What if we reframed events like the rise of AI less as disruptive events requiring immediate action and reconsidered paces of adoption, implementation, allocation of resources within the larger context of university aims and motivations?
An emphasis on labor literacy slows down the AI adoption process and the aspirations AI Evangelists have for a more efficient university, but I would argue that the various laborers in higher ed would benefit from leaning in to some of the frictions generated (sorry) by AI’s increasing ubiquity in lieu of sanding them down with rosy pictures of an inevitably human-centered future alongside this latest set of technological innovations. We’ve seen the dark side of the “move fast and break things” approach too many times now. Many laborers in higher ed have organized in part to encourage their institutions to slow down and consider the long-term implications of their decisions.
That’s where my head is at in March 2025. I will try to write a bit more about this topic, though the format and aims might vary. For now, I thought it was important to outline my own emerging investments in these three areas and where I see them as overlapping and intersecting.
I’ll close by noting that I would not describe myself as an AI Evangelist, Apologist, or Advocate, but I have seen some promise in particular applications of AI tools in particular higher education contexts. Much of that work is preliminary or exploratory, though! I have spent a fair bit of time assessing particular AI tools within the context of my own work as an instructional designer and have found benefits to certain applications to tools to more mundane tasks like, say, developing a meeting agenda. But I haven’t neatly integrated any tools within workflows. I’m also happy to hear from colleagues with their own experiences and contexts as they consider where AI tools may fit into their own efforts, and I try to balance providing my own perspectives on these topics with the realities of serving a pretty wide and varied community of educators.
Note: The views expressed in this post may not be shared by my employer or all of my colleagues and should not be regarded as an institutional or departmental perspective. If you’d like to talk more, feel free to email me at jimmcgrath[dot]us[at]gmail