Earlier this year I wrote a blog about the continuing debate across higher education on whether — and how — university students should be taught to properly use generative AI, “How Should University Students Be Taught the Proper Use of AI.” I mentioned in the blog that the debate about AI reminds me of my own early experiences with computers as a physics student at the University of Chicago in the 1960s — a time when the legitimacy of using computers as a tool for conducting research in physics and other disciplines was also in question.

At the time, the use of computers for scientific research was still relatively new. My graduate student advisor was professor Clemens Roothaan, one of the pioneers in the use of computers in physics and chemistry research. Some older physics professors looked askance at the growing use of computers, and told me that this wasn’t “real physics” — that is, the kind of pencil-and-paper physics they had grown up with over the past few decades. Their reaction echoes today’s discussions about the proper use of AI-based tools in higher education.

“As graduates leave our campuses, companies are expecting to welcome a cohort of digital natives who’ll be ready to use AI with confidence,” wrote Miami University management professor Megan Gerhardt in a Harvard Business Impact article, “Higher Ed Is Sending Mixed Signals of AI.” “Instead, they’ll be getting new degree-holders who spent the last four years receiving warnings from at least some of their faculty that AI use is cheating, not a critical skill for their future careers.”

Gerhardt writes that she sees this gap from two vantage points. As a professor at Miami University where she teaches change management and leadership to undergraduates. And as a consultant and strategist for organizations around the world, for whom she helps reframe their generational challenges as opportunities for innovation and learning.

“The distance between those two worlds has never felt wider.” As a concrete example of this gap, she cites a faculty meeting earlier this year where several of her colleagues discussed how to shut down the use of generative AI in their classrooms. That same week, one of her corporate clients asked her why their Gen Z interns were reluctant to use their company’s AI platform. “The irony was not lost on me,” she added.

Students are paying the price for these mixed signals. A recent graduate shared her direct experience: “There was a significant knowledge gap that I only recognized once I began my internships. In many classrooms, AI is stigmatized. However, during my internships, I was strongly encouraged to use it. As a result, I fear that I may struggle to adjust to professional environments.”

What the mixed messages on AI cost students

“Our hesitation in allowing the use of AI in our classrooms is understandable,” said professor Gerhardt. It’s been very challenging to get ahead of the impact chatbots are having on how students learn. “The teaching tools many of us spent careers developing have been upended in a matter of semesters.”

What are those teaching tools that AI might now be upending?

Teaching students how to think, said Gerdhardt. “In over two decades of experience as a faculty member, the phrase I have heard most consistently is that our unique value is not teaching students what to think, but how to think — or, on our more ambitious days, how to think about thinking. When it comes to AI, that means embracing a new and necessary role: helping students learn to evaluate sources, develop lenses through which to interpret and filter an overwhelming flood of information, figure out what mattered and what was credible, and analyze it in ways others might not.”

Stanford University postdoctoral research fellow Bharat Chandar makes a similar point in his Substack essay, “Will AI Create a Generation of Non-Thinkers?” “Recall staring blankly at a page, struggling to come up with an answer to an essay prompt. Formulating and articulating a thought might have taken hours, each sentence revised over and over. Working through writer’s block to craft a compelling argument was a painstaking rite of passage towards becoming an effective thinker and communicator.”

“Do students today have this experience?,” he asked. “If AI can write our essays, what happens to human thought?”

Because AI feels threatening to higher education, we have reacted defensively, leading students to fear and resist the very tools they are now being handed and expected to use outside our classrooms,” noted professor Gerhardt. “This generation outperforms every other in understanding AI and its relevance, yet scored the worst of any generation on critically assessing its limitations.”

Based on her experience as well as that of fellow educators, Gerdhard offers a few strategies to try in the classroom. Let me summarize the classroom strategies she uses to teach students how to best interact with AI.

Classroom strategies

Have an open conversation

Remove the stigma and suspicion surrounding usage of AI. “I now begin each semester by inviting my students to engage with me and each other about the realities of learning and working with such a powerful tool at their life and career stage. My work in intergenerational collaboration has taught me that there is always something important to learn by being genuinely curious about how the experience of those significantly younger (or older) than me may create a different perspective on an important challenge.”

She asks students some questions that she’s genuinely curious about, such as “How is AI helping you learn right now? What is it good for? Where does it fall short? What are you concerned about?”

She shares her own personal experiences, including where AI has been helpful in her research and teaching and where it’s been a poor substitute for human thinking.

“For example, I love using AI to critique my ideas and arguments in my research, surfacing weaknesses long before a reviewer might find those shortcomings. I’ve discovered that I don’t like using AI in my final draft writing; I have a unique voice that helps me stand out, which I do not want to lose.”

Strengthen students’ AI fluency

Gerhardt illustrates how to learn AI fluency with two concrete examples:

1. Pose a question you are curious about to the AI tools of your choice. Instruct it to support its response with peer-reviewed, scholarly sources. Then find and read those primary sources yourself. Do the sources exist? If so, do they support what the AI claims. Or, did the AI tool hallucinate or get something wrong? Given that prompting is now an important skill for interacting with AI, she asks students who received a valid, accurate AI answer what they specifically asked AI and how they asked it.

2. When assigning a research paper, she encourages students to begin by prompting AI to act as a research assistant. As part of the assignment, she requires students to include “a thorough appendix that discusses how they verified AI output, where they pushed back, and how their own thinking shaped and changed what AI initially gave them. The appendix is not a formality. It is the assignment within the assignment.”

These checks and balances have helped the students realize “the important role their own human intelligence plays in using AI responsibly and well.”

Redesign assignments for AI-enhanced learning

“One of the hardest issues for faculty to come to terms with right now is that the assignments we used before AI can no longer assess learning in the same way. I truly loved the struggle and discovery that used to occur via the take-home case analyses I assigned in my leadership and change management courses. But it is simply unrealistic now to expect that students will not upload the case and my instructions into their favorite AI software and have a respectable paper in less than five minutes. I’m not happy about it, but I’ve started using that discontent to develop new assignments that incorporate AI to teach students how to tackle the messy, ambiguous challenges of leadership.”

Equip students to use AI responsibly

She reminds students that with great power comes great responsibility. “For better or worse, we are surrounded by examples of what might happen if the power of AI is not used ethically or well, and those examples belong in our classrooms.”

It’s important to share with students concrete examples of irresponsible uses of AI and the legal and reputational consequences. The message for students should be very clear: “When you use AI, you are ultimately still responsible for what appears in the final work product, whether in our classroom or to a client.”

Create space for exploration

“When it comes to AI, we are all building the plane as we fly it,” said professor Gerdhardt in her concluding recommendation. “That means I am learning alongside my students, and I tell them so. What I have found is that this admission itself is generative: It opens up exactly the kind of honest and collaborative thinking that higher education has always been built to foster. We do not need to have all the answers about AI. We need to model what it looks like to pursue them carefully, critically, and with genuine intellectual curiosity. That is what higher education is made for. And right now, it is exactly what the world needs most.”