Higher education and artificial intelligence (AI) are a hot topic these days. It is not a subject to shy away from considering the impact AI will have on how and what we learn and future employer criteria to secure a job.
In Part 1 of this 3-part look at AI and education, guest author Harper Lane described the educational divorce happening in universities today: a conflict between two educational goals, teaching students to reason on their own, and teaching students to meet future social needs, namely jobs and fitting into society.
In Part 2, Harper states that the current educational model is no longer about learning. AI usage in post-secondary institutions is rapidly on the rise. The AI genie has been let out of the bottle, and educators can no longer put the plug back in.
A recent poll of students enrolled in post-secondary business programs at one school had the majority requesting to learn structured AI preparation to master the technology for work while still preserving the ability to think and reason. This is the two-edged sword that higher education providers face in the 21st century.
Harper’s words follow.
Why Education is No Longer About Learning
In the first part of this series, we examined the growing conflict between two traditional goals of higher education: developing subject-specific knowledge and preparing students to use modern technology effectively.
AI has forced universities to choose between two objectives that reinforced each other. There was a third goal, hidden in plain sight. It did not belong to professors, administrators, or employers. It belonged to the students themselves.
The Secret Third Goal of Education
Most students do not take on massive debt solely because they want to acquire knowledge for its own sake. They attend because they want a degree, a career, financial stability, and access to opportunities otherwise unavailable to them.
While this attitude is understandable and practical, there is something profoundly lost when education becomes transactional. A society depends on more than workers collecting credentials and acquiring marketable skills. It depends on citizens who are curious, informed, and genuinely interested in understanding the world around them.
Even so, the university system has continued to function because personal advancement and genuine learning have aligned. A student who wanted just the credential usually had no choice but to develop some degree of mastery along the way.
Changing the Shortest Path to Success
When assignments can be completed without deeply engaging with the material, the incentive structure changes. That’s what is happening with AI.
Students who are already balancing jobs, internships, extracurricular activities, and social obligations quickly realize that learning is no longer the most efficient path toward the outcome they actually want. The most efficient path is completing the requirement, and AI is used primarily because it saves time. In other words, learning becomes vestigial. It remains the stated purpose of the system, but it is no longer necessary for navigating the system successfully.
This helps explain why many students embrace AI even when they recognize that it may undermine learning. From their perspective, the university (and employers) continue to signal that grades, credits, and credentials matter more than understanding.
How AI Exposes the Incentive Structure
The uncomfortable reality is that AI did not create this problem. It merely exposed one that already existed. Long before ChatGPT, students were cramming for exams, memorizing information long enough to complete assessments, and forgetting much of it shortly afterward. AI has not changed the incentives. It has simply revealed them with unprecedented clarity.
Nor is this issue confined to students. Universities often reward course completion more than mastery. Employers frequently use degrees as screening mechanisms rather than evidence of deep expertise. The entire ecosystem increasingly focuses on measurable outcomes such as grades, credits, credentials, graduation rates, and job placement statistics.
When every participant in the system prioritizes signals of success over the difficult and often invisible process of learning, it should not be surprising that students do the same.
AI did not invent credentialism. It merely made optimization easier. That is what makes this current moment so consequential.
AI has exposed a contradiction that higher education could previously afford to ignore. Universities claim that learning is the purpose of education, yet many of their structures reward something quite different.
How Do We Begin Valuing Learning Again?
If universities want students to value learning again, they cannot merely prohibit AI. They must redesign courses so that learning once again becomes the most reliable path toward success.
That challenge will require more than new policies or stronger enforcement. It will require universities to rethink how learning is evaluated in the first place. If traditional assignments no longer provide reliable evidence of mastery, then institutions may need to rediscover older forms of assessment while inventing entirely new ones.
The question is no longer whether students will use AI. They will. The real question is how universities can build systems where understanding remains valuable even when information and finished work are available at the push of a button.
In Part 3, the topic looks at how higher education can meet future societal needs with onboard AI.
