In late November 2022, ChatGPT was released as a free research preview. It reached 1 million users in just five days, a record at the time for any tech product.
And since then, ChatGPT – and generative artificial intelligence, more broadly – has continued changing the world. Fierce competition erupted between countries to see who is going to get ahead, highlighted with the release of the foreign-developed DeepSeek – which was referenced as a “Sputnik” moment – the resulting Big Tech company layoffs and dwindling post-graduation job prospects, even for STEM grads.
Job prospects, even with a degree in hand, aren’t the only aspect of higher education being affected.
GenAI has demonstrated the ability to solve mathematical problems and respond to case study questions which are designed to develop critical thinking in students. In a sense, it has replaced the idea of there being only one expert in the room, the professor.
This has come at a time when universities are under fire with questions around value for the cost from the student side and diminishing contributions to innovation on the industry side.
Given the increased demand for specialized degrees (e.g., master of science in business analytics) and the expansion of technical courses in areas such as applied analytics, we fully recognize that nearly every student in our classrooms is using GenAI in some form.
The introduction of AI in the classroom has caused the rise of “copy and paste” syndrome: AI-written documents and slides with rocket or idea emojis and terms and concepts that students themselves wouldn’t be able to explain without an AI-generated script.
The result? Limited understanding, surface-level learning and possible embarrassment when questioned further.
Another outcome is a demand for and expectation of something typically reserved for youth sports: participation trophies. GenAI tends to provide overly positive feedback that may mislead a student into consistently thinking their initial ideas are logical and sound, stifling deeper questioning, and in turn, stunting problem-solving.
But how can we expect anything different with unclear university policies on its use, a competitive educational environment, tight coursework deadlines, an internship and
often in the case of part-time students, other competing demands like full-time work,
and family?
Add to that the fact that “everyone else is doing it,” and you have a perfect recipe for widespread adoption.
While the negative consequences are real, it’s naive to assume they’re entirely harmful. Students gain meaningful benefits: easier access to advanced technical skills, a collaborative tool or “artificial tutor” for tackling complex problems, support for conceptual understanding, alternative explanations and an empowering sense of independence.
These are good things. But questions still remain.
In the recent Future of Jobs Report by the World Economic Forum almost 70% of surveyed employers identified “analytical thinking” as the number one core skill employees need; this is in part a byproduct of technological advancements such as those in AI. Yet a recent MIT study suggests that GenAI actually detracts from our cognitive abilities. Researchers reported, “…over-
reliance on AI can erode critical thinking and problem-solving skills: users might become good at using the tool but not at performing the task independently to the same standard.”
The real question is, “Is what we are teaching today going to be relevant for tomorrow?”
We would be foolish to think that we can avoid AI completely and such an approach will not set up students who will graduate into an AI-saturated world for success. Instead, we must learn how to thoughtfully embrace it.
After all, if we genuinely view GenAI as an innovation that drives change and creates value in industry, isn’t it our responsibility as educators to evolve alongside it?
This could lead to courses centered on prompt engineering and optimization, or on building the skills needed to ask the right questions and effectively interface with GenAI to drive value-creating outcomes.
But at the same time, we should be studying and explaining to students how the Large Language Models (LLMs) that underpin technologies like GenAI work so that the user – the student – understands its limitations.
In other words, we should emphasize to students that humans remain the ultimate decision-makers, that choices have consequences and require rigorous evaluation of data and information and that GenAI is a collaborator, not a substitute for critical thinking, discipline or deeper learning.
Alfonso Berumen is a practitioner of decision sciences at the Pepperdine University Graziadio Business School and an academic affiliate with Libra Analytics.
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