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The AI Conversation in Higher Education Is Changing
For universities, the conversation around artificial intelligence is quickly moving beyond whether students should be allowed to use it. The more important question is becoming whether institutions are adequately preparing students to use AI once they enter the workforce. That question was at the center of a recent QuantHub conversation bringing together information systems faculty and academic leaders to discuss what AI means for students, instructors, and the future of higher education.
Hosted by QuantHub Director of Marketing Kellie Weed and led by Director of Partnerships Jacob Krause, the conversation included perspectives from faculty and academic leaders at the University of North Carolina Greensboro, North Carolina A&T State University, and Tennessee State University. The goal wasn’t to present a single solution. Instead, faculty were invited to talk openly about what they are seeing in their classrooms, where students are struggling, and how institutions can learn from one another as AI becomes a more permanent part of education and work.
One of the first questions raised was whether the greater risk is students using AI too much or students not learning to use it at all. The faculty responses revealed that the issue isn’t quite that simple. Students need opportunities to work with AI, but they also need to understand what they’re doing, why they’re doing it, and when AI is or isn’t appropriate. The challenge for higher education is finding that balance.
Moving From AI Access to AI Fluency
Dr. Anita McCoy of UNC Greensboro argued that avoiding AI altogether may ultimately present the greater risk. Universities are preparing students to enter careers where AI is increasingly present, and sending graduates into that environment without experience using these tools could leave them at a disadvantage. At the same time, simply giving students access to AI isn’t enough. They can still use it poorly, misunderstand its capabilities, or accept its output without thinking critically about the result.
Dr. Lakshmi Iyer, Professor and Head of the Department of Information Systems and Supply Chain Management at UNC Greensboro, described a more purposeful approach. Rather than allowing students to use AI simply to arrive at an answer, faculty can connect AI activities directly to learning objectives. Students should still be expected to reason, reflect, explain their decisions, and demonstrate that they understand the underlying problem. AI can contribute to that process, but it shouldn’t replace it.
That distinction becomes particularly important when students leave the classroom. In a professional setting, being able to produce an AI-generated answer is very different from being able to evaluate, defend, and apply it. A student who has learned to treat AI as an easy button may struggle when an employer expects them to explain their reasoning. AI fluency, then, isn’t simply about knowing which tool to open or what prompt to type. It’s about knowing how to work with AI while remaining responsible for the outcome.
Faculty Are Learning Alongside Their Students
Universities have also had to evolve their own thinking. When generative AI first became widely accessible, much of the educational conversation centered on academic integrity. Faculty and administrators understandably wanted to know what AI meant for assignments, assessment, and cheating. But as the technology became more embedded in professional life, the discussion began to expand. The question was no longer only how to prevent inappropriate AI use, but how to teach appropriate use.
The faculty participating in the conversation described different ways their institutions are responding. Some have developed courses specifically focused on AI, while faculty are also being encouraged to integrate relevant AI tools into existing courses. Rather than requiring everyone to use a single platform, instructors may have room to experiment and determine which technologies make sense for their content and learning objectives. That flexibility is especially valuable while AI tools and capabilities continue to change rapidly.
Adoption among faculty remains a work in progress. One academic leader described a majority of faculty in his department as already experimenting with some degree of AI integration. Complete adoption may not be realistic, nor does every course necessarily require the same approach. What matters is creating an environment where faculty can explore the technology thoughtfully, compare experiences, and develop practices that preserve learning while preparing students for what they will encounter outside the university.
Students Are Starting From Very Different Places
One of the biggest complications for educators is that there is no single level of student AI readiness. Dr. Iyer described seeing a wide spectrum of abilities. Some students are already developing bots and participating in sophisticated AI projects. Others are still at the stage of entering an assignment into an AI system and essentially asking it to help them get a good grade. Both students may technically be “using AI,” but their level of understanding is dramatically different.
That gap could become even more pronounced as younger students enter college. Dr. McCoy compared AI adoption to the way people learned to use smartphones: many young people will simply pick up the technology and begin experimenting with it. Future students may arrive on campus having used AI throughout high school or even earlier. Familiarity, however, shouldn’t be confused with fluency. Someone can use a smartphone every day without understanding much about how the technology works, and the same can be true of AI.
This creates an important opportunity for educators. Instead of assuming students understand AI because they have used ChatGPT or another generative tool, institutions can provide structure around that experience. Students can learn about the benefits and limitations of AI from the beginning, develop better habits, and understand the responsibility that comes with using AI-generated information. As one participant put it, students should learn how to use AI properly whenever they begin using it, regardless of their age or academic level.
Preparing Graduates to Work With AI
Ultimately, the conversation returned to the reason universities are wrestling with these questions in the first place: students are preparing to enter a workforce that is changing. AI is increasingly being used for analytics, automation, communication, and other business functions. It is no longer a technology relevant only to developers or highly technical roles. The discussion framed AI fluency as an emerging foundational literacy that graduates across disciplines will increasingly need.
That reality naturally creates anxiety about jobs. Students see headlines about automation and may wonder whether the careers they’re preparing for will still exist. Faculty in the discussion emphasized a different way of looking at the change. Instead of teaching students to fear AI, universities can help them understand how their chosen professions are evolving and identify where AI can complement their expertise. Dr. Iyer summarized the idea particularly well: the greater competitive threat may not be AI itself, but another person who knows how to use AI effectively.
The challenge for higher education, therefore, isn’t to choose between embracing AI and protecting traditional learning. It is to do both. Students still need critical thinking, subject-matter expertise, reasoning, communication, and the ability to explain their work. Increasingly, they also need to understand how to apply AI without surrendering those abilities to it. Universities that can bring those pieces together will be better positioned to prepare graduates not simply to enter an AI-influenced workforce, but to participate in shaping what that workforce becomes.
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