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Creating Space for a Shared AI Conversation
Artificial intelligence is no longer a future-facing topic for accounting educators. It is already shaping the expectations students will face when they enter internships, interviews, and full-time roles. That reality framed a recent QuantHub webinar focused on how faculty can help students build practical, responsible AI skills without losing sight of the fundamentals that make them effective accounting professionals.
Kellie Weed, Marketing Director at QuantHub, opened the session by explaining that many universities are navigating the same questions, often without enough opportunity to compare notes with peers. “A lot of universities feel like they’re in this kind of silo of trying to figure out how do we implement AI education?” she said. The purpose of the webinar was to create space for that exchange: a conversation where faculty could share what they are trying, what is working, what is not, and what challenges are still unresolved.
Rather than positioning the session as a product presentation, Kellie emphasized that the goal was to give educators the floor. She described the webinar as a place to discuss the problems keeping faculty up at night and to better understand how AI education is unfolding across institutions. That collaborative tone carried through the conversation as speakers explored student readiness, curriculum design, responsible AI use, and the growing gap between classroom preparation and workplace expectations.
“We’re hearing from a lot of universities that they’re trying to figure out how to implement AI education, but there isn’t always a lot of connection across institutions.”
Kellie Weed, Marketing Director at QuantHub
Rethinking the Real Risk of AI in the Classroom
Jason Rupert, Strategic Advisor at QuantHub, led the discussion by asking participants to consider a central question: is the bigger risk that students use AI too much, or that they do not learn how to use it well at all? For Jason, the answer is tied to the pace of change in the profession. “The industry is changing significantly,” he said, “often much faster than education is changing.” That mismatch creates pressure for colleges and universities to rethink how quickly and intentionally they prepare students for AI-enabled work.
Jason brought more than 15 years of experience in ed tech to the conversation, with a background supporting both higher education and corporate clients. He explained that QuantHub sits in a unique position, hearing from employers about what they wish graduates could do and from educators about what students are ready to learn. That perspective has made one thing clear: students need more than casual exposure to AI tools. They need foundational skills they can carry with them as specific platforms and workplace expectations continue to evolve.
Jonathan Kern, Accounting Instructor at the University of Oklahoma, pushed the conversation beyond the common fear that students are simply overusing AI. “I don’t really think that’s like the question or the problem,” he said. “I think the problem is students not knowing how to use AI well, like they know how to use it like Google.” His point captured a recurring theme of the webinar: students may know that AI exists, but that does not mean they know how to use it in a professional, accurate, or ethical way.
“I don’t really think the problem is students using AI too much. I think the problem is students not knowing how to use AI well.”
Jonathan Kern, Accounting Instructor at the University of Oklahoma
Building AI Literacy Without Losing the Fundamentals
Jason agreed with Jonathan’s point, noting that search engines have trained users to expect helpful results from rough or incomplete inputs. AI tools require a different level of clarity. Students need to understand how to write effective prompts, provide context, set constraints, evaluate outputs, and verify results. In accounting, where precision matters and professional judgment is essential, AI literacy cannot stop at experimentation. It must include the habits of skepticism, accountability, and review.
James Clifton, Professor in Accounting at North Dakota State University, brought a practical classroom perspective to the discussion. He teaches individual income tax, advanced fraud, and applied professional research, giving him a broad view of how AI might show up differently across accounting courses. In tax, he emphasized the importance of fundamentals. In professional research, he expects students to use AI, but only if they can write strong prompts, describe what they need, set constraints, fine-tune their work, and verify their sources.
James was especially direct about policies that attempt to ban AI outright. “You are lying to yourself if you think that’s going to hold,” he said. “The students are going to use it. I’m not going to waste my time trying to police them. It’s, I need to instead say responsible use of AI.” For him, the better instructional approach is not prohibition, but guidance. Students need to learn when AI can save time, when it can introduce risk, and how to stand behind the work they submit no matter where the first draft or initial analysis came from.
“I’m not going to waste my time trying to police them. I need to instead say responsible use of AI.”
James Clifton, Professor in Accounting at North Dakota State University
Moving From Exposure to Applied Practice
The discussion also surfaced questions about where AI instruction belongs in the curriculum. Some faculty are embedding AI assignments into existing courses, while others are considering whether accounting programs may eventually need dedicated AI-focused courses. Jonathan described AI literacy as “the lowest level that we need to provide our students,” especially in entry-level courses. As students move into intermediate and advanced coursework, the expectation may shift from basic literacy toward applied analysis, research, and discipline-specific judgment.
Jason explained that QuantHub’s approach is designed to support both foundational and discipline-specific learning. The goal is not to train students on one tool or platform, but to help them understand AI concepts, ethics, responsible use, tool selection, and human-AI collaboration. “This is not replacing you,” he said. “This is a learning tool, learning partner.” That distinction matters in accounting education, where students must learn to use AI as support for their thinking rather than a substitute for it.
Rich Motz, Assistant Professor of Accounting at the University of North Carolina Wilmington, connected the AI conversation to a familiar issue in accounting education: skill retention. He explained that students may complete an Excel lab early in the curriculum, but by the time they reach upper-level accounting courses, many have forgotten key skills like XLOOKUP or pivot tables. The same risk applies to AI. If students encounter it once and then do not use it again in meaningful ways, the learning may not stick.
“I want students to manually find the information first, so they know what they’re looking for. Then I want them to do it with AI, compare the difference, and verify what they get out of AI with what they got manually.”
Rich Mautz, Assistant Professor of Accounting at University of North Carolina Wilmington
Preparing Students for AI-Supported Professional Judgment
To address that challenge, Rich described designing assignments based on how professionals are already using AI. “One of the things that I try to do is I will go to professionals and say, what do you use AI for?” he said. Those conversations help shape classroom activities where students complete a task manually, use AI to complete or support the same task, and then compare the results. For example, students may review financial statements, lease agreements, or public filings by hand before evaluating how accurately AI identifies the same information.
That approach keeps human judgment at the center of the learning process. Students are not simply asking AI for answers; they are learning what the answer should look like, checking whether the tool performed accurately, and reflecting on the differences. In doing so, they build both technical familiarity and professional skepticism. They also begin to understand that AI is most useful when paired with a strong foundation in the subject matter.
For accounting programs, the path forward is not a choice between fundamentals and AI. It is a matter of teaching both in relationship to one another. Students need to know the concepts, rules, and reasoning that define the profession. They also need to know how emerging tools can support, distort, or accelerate that work. As the conversation showed, the goal is not to produce students who blindly trust AI, or students who avoid it altogether. The goal is to prepare graduates who can use AI responsibly, verify what it produces, and bring human judgment to every decision that matters.
As conversations like this continue across higher education, one thing is becoming increasingly clear: AI literacy is no longer a future consideration—it’s a present-day necessity. At QuantHub, we partner with colleges and universities to help students, faculty, and staff build the foundational AI knowledge needed to thrive in an evolving workforce. Through engaging, accessible learning experiences, institutions can equip learners with the practical skills to understand AI, use it responsibly, and apply it effectively across disciplines. Whether you’re looking to introduce AI literacy across campus, support accreditation requirements, or prepare students for the careers of tomorrow, QuantHub is committed to helping bridge the gap between emerging technology and workforce readiness.
Ready to bring AI literacy to your campus? Learn how QuantHub helps institutions build AI-ready graduates through scalable learning experiences that develop the critical thinking, ethical decision-making, and practical AI skills employers are increasingly demanding.