Building AI Literacy in Higher Education: Programs, Practice, and Community copertina

Building AI Literacy in Higher Education: Programs, Practice, and Community

Building AI Literacy in Higher Education: Programs, Practice, and Community

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Episode 38: Building AI Programs While the Ground Is Still MovingShould a business school build AI-specific degree programs, or work AI into the curriculum it already has? Tom Stafford, professor and chair of Management Information Systems at the University of Memphis, made his choice and explains why he made it.Craig and Rob are joined by Dr. Tom Stafford, who came back to Memphis after nine years at Louisiana Tech and is launching a four-course graduate certificate in applied AI this fall, plus a five-course concentration in the MSIS program built on the same courses. The conversation starts with the mechanics of standing up a program quickly (prompt engineering and AI-assisted application development first; AI-assisted database and analytics in spring) and widens into the harder questions: whether AI will diffuse into everything the way e-commerce did, why the "haters" may not be the problem people think they are, and what universities actually owe faculty and staff who want to learn this. Along the way, Rob describes a staff AI workshop he ran at Washington State, Craig coins "setting the context for discovery," and the cool uses segment turns up a dynamic news site, an interactive game about professional dress, and a blues band website built by someone who calls himself an AI neophyte.What you'll hearWhy Memphis went program-specific rather than curriculum-wide. Tom is candid that the driver was enrollment. International graduate enrollment fell hard after visa issues, and his brief coming in was to build something that would draw local students. Being first through the state curricular approval process mattered; so did the fact that International Paper, FedEx, AutoZone, and an xAI footprint sit in the same city.The e-commerce analogy, and where it breaks. Rob asks the five-to-ten-year question: won't AI just become the way business is done, the way e-commerce did? Tom, who arrived at Memphis as the e-commerce expert and watched exactly that happen before tenure, agrees it's the medium-term outcome. Craig pushes back; e-commerce spread incrementally, while the jump from chatbot to agentic AI is a discontinuous change whose horizon keeps moving.Requiring AI instead of policing it. Tom decided years ago not to chase students who use AI. He requires a term paper produced with an LLM, and if it isn't excellent, the student hasn't done the work. His qualitative data on that requirement shows students finding it useful and also finding it harder than expected; "way more work than I expected" is a common response.Where administration helps and where it gets in the way. Rob makes the case for grassroots communities of practice over top-down workshops that promise to teach everything. Craig agrees but worries organic groups stay small, and argues administration's real jobs are spreading the practice past the initial circle and signaling that experimentation is an acceptable use of faculty time.Cool uses. Craig walks through the AI Goes to College daily briefing site he built with ChatGPT's Sites feature, fed by a scheduled task that scans higher ed AI news every weekday. Rob built an interactive game with the same tool to teach students what business casual actually means. Tom built and now runs bluetattooband.com himself.The time-and-a-half problemThe most useful idea in the episode may be Tom's comparison of AI adoption to the distance education buildout of the past two decades. Learning to use AI for your work costs more time than simply doing the work; universities recognized that with online course development and paid for it in course releases and stipends. They have mostly not done the same for AI. Tom's point is that faculty resistance often isn't ideological at all; it's a schedule problem dressed up as one.Craig's related concern lands on staff rather than faculty. Faculty are effectively independent contractors, so efficiency gains stay with them. Staff efficiency gains get noticed, and the reward is more work. His suggestion is that this is manageable at the department chair or associate dean level: when AI takes the tedious parts of someone's job, fill the space with work they find worth doing rather than more tedium.Episode highlightsTimestamps include the 41-second pre-roll; verify against the audio before publishing.(03:51) Tom on scoping the program: "We're not equipped in a business school to help anybody become an AI wizard and build algorithms and design platforms."(20:31) Craig on why this isn't e-commerce: "The knowledge work of the future is not gonna be the knowledge work of the past."(23:01) Tom on academic integrity: "I decided a long time ago, I wouldn't worry about trying to catch people doing it, I'd force them to do it."(30:01) Craig coining a phrase: "I think we need to set the context for discovery."(44:01) Tom on the real barrier: "It's more work to learn how to use AI to do your work than it is to simply do your work."(48:56) Rob on breaking down silos: "None of us should ...
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