QuantHub
QuantHub
Support
Bringing Higher Education Together Around AI
Artificial intelligence is moving faster than most institutions can adapt, creating both opportunities and challenges for higher education. During QuantHub’s recent Higher Education Round Table, faculty members, deans, and academic leaders gathered to discuss one central question: How can universities prepare students for an AI-powered workforce while preserving critical thinking and academic integrity?
Opening the session, QuantHub Director of Marketing Kelly Weed emphasized that the goal was not a formal presentation but a collaborative conversation. She noted that many institutions are facing similar questions about AI adoption, yet often approach them in isolation. By bringing educators together, the discussion created a space to share challenges, successes, and practical strategies.
Joe Derario, Director of Partnerships at QuantHub, set the stage by drawing on his experience working with major organizations and helping institutions develop AI readiness. He explained that AI has quickly evolved from a specialized technical skill into a foundational literacy. As he put it, “The gap isn’t whether AI is being used, it’s whether people understand how and when to use it well.”
“The gap isn’t whether AI is being used, it’s whether people understand how and when to use it well.”
–Joe Derario, Director of Partnerships at QuantHub
The conversation began with a simple but revealing question: What is the bigger risk today—students using too much AI or not learning how to use AI at all? While attendees offered different perspectives, the discussion quickly revealed that the issue is far more nuanced than a simple either-or choice.
Mark McNeely, professor at UNC and advisor to QuantHub, highlighted one of the most overlooked challenges facing students today: inconsistent AI policies. “Students being confused about the AI policy” may be one of the biggest risks, he explained. With different professors adopting different approaches—and sometimes different rules for different assignments—students often struggle to understand what is acceptable and what could potentially be viewed as misconduct.
Several participants also pointed out that AI itself means different things to different people. Professor Abhijit Chandra of Florida Atlantic University observed that some educators think of AI primarily as machine learning and data analysis, while others focus on generative AI tools like ChatGPT. He argued that universities must move beyond treating AI as simply a faster search engine and instead help students demonstrate meaningful problem-solving skills that employers will value.
“The top tier students, they clearly see this as something that’s going to help them. That’s a job augmentator. They are pushing themselves to get better on this because they think it’s going to give them a huge leg up in the job market.”
— Abhijit Chandra, Professor, Florida Atlantic University
Where Students Are Really Learning AI
As the discussion continued, participants explored a critical question: Where are students actually learning AI today? The consensus was that, for the most part, students are teaching themselves.
McNeely noted that while some universities offer AI-related courses, there is often no coordinated curriculum guiding student development. “They’re just kind of learning it on their own by using it,” he said. While students may understand how to enter prompts, many still struggle with evaluating outputs, identifying inaccuracies, and applying AI ethically and intelligently.
Faculty members echoed this concern. Kassia Wosick from Texas A&M University at Galveston compared today’s AI landscape to the early days of Microsoft Office. Decades ago, institutions explicitly taught students how to use Excel and Word; eventually those tools became assumed competencies. Wosick suggested that AI may follow a similar trajectory, but emphasized that higher education has not yet reached that point. She also noted that upcoming AACSB accreditation standards will require business schools to incorporate AI literacy into their curricula, creating additional urgency for institutions.
Derario shared a personal example, explaining that even his own teenage daughter is learning AI skills outside of school through social media, group chats, and online communities. The observation reinforced a key theme of the discussion: students are gaining exposure to AI, but that exposure is often informal, inconsistent, and disconnected from the expectations employers have for responsible and effective use.
“The gap isn’t whether AI is being used, it’s whether people understand how and when to use it well.”
— Joe Derario, Director of Partnerships, QuantHub
AI as a Job Transformer, Not a Job Destroyer
The conversation then shifted toward the workforce implications of AI. Rather than focusing on whether AI will eliminate jobs, participants explored how it is likely to transform the nature of work itself.
Several educators argued that students often lack the context needed to understand AI’s impact on careers. Siva Viswanathan of Lehigh University suggested that it is the responsibility of educators to help students understand the conditions under which AI acts as a “job destroyer” versus a “job transformer.” Without that framework, students may struggle to interpret the rapid changes occurring around them.
Abhijit Chandra observed an interesting divide among students. Top-performing students often view AI as an opportunity to gain a competitive advantage, while others are more likely to see it as a threat. “The top tier students, they clearly see this as something that’s going to help them,” he said, describing how many are actively building AI skills to differentiate themselves in the job market.
Daniel Hall, Dean of the Phillips School of Business at High Point University, offered a broader economic perspective. He argued that AI resembles previous technological disruptions that ultimately created more opportunities than they eliminated. “The jobs taken are very clear and visible and upfront, and the jobs created are not clear, hard to predict,” Hall explained. His message was clear: adaptability and continuous learning will be more valuable than trying to predict specific future job titles.
“Yeah, they know how to enter prompts, but I’m not sure they know how to use it intelligently and ethically and be able to judge what’s coming back as, is this true or not?”
— Mark McNeely, Professor, University of North Carolina
As the session approached its conclusion, participants focused on one of the most important educational questions of all: How do universities teach AI understanding without turning every program into a technology program?
Many agreed that the answer begins with emphasizing foundational concepts rather than simply teaching tools. One participant argued that students must understand why AI works the way it does before they can use it effectively. Concepts such as probability, retrieval-augmented generation (RAG), prompt quality, and data reliability provide students with the context needed to evaluate AI outputs rather than blindly accepting them.
The discussion also highlighted innovative classroom practices already emerging across universities. Several faculty members described assignments that require students to submit the prompts they used, the platform they selected, the responses they received, and their interpretation of the results. This approach shifts assessment away from simply producing an answer and toward demonstrating reasoning, judgment, and reflection.
Ultimately, the roundtable revealed a growing consensus among higher education leaders. AI literacy is no longer optional, but neither is critical thinking. The institutions that succeed will be those that help students understand not just how to use AI, but when to trust it, when to challenge it, and how to apply it responsibly in a rapidly changing workplace. As the discussion made clear, the future belongs not to those who simply use AI, but to those who know how to use it well.
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.