Purdue University made history last month as one of the first higher ed institutions to boldly offer a cohesive vision for the future of AI in classrooms, releasing a strategy that proactively incorporates AI into curriculum and aims to make every student “AI competent” by graduation.
The announcement resulted in its fair share of criticism, with one skeptic calling it a “shortcut for incompetent people.” But at a time when many institutions still lack basic guidelines for how to approach AI, the move represents a much more proactive shift toward something that higher ed sorely needs: a clear, cohesive vision for artificial intelligence on campus.
Because at too many colleges and universities across the country, we’re already seeing the consequences of what happens without one.
Despite its increasing role in the economy (and in students’ lives), many colleges and universities stop at plagiarism while developing their AI policies—or allow competing AI policies to flourish among departments that tend to focus on how to limit AI use.
What most aren’t doing is encouraging pathways to use AI effectively within those boundaries, or communicating a bigger vision for how faculty and students should use the technology on campus.
The result of this is a murky landscape that leaves faculty and students confused—and contributes to an increasingly adversarial relationship between them. According to one recent study, nearly 90% of college students use AI in their studies, but 31% say they aren’t sure when they’re allowed to use it.
“Am I a teacher, or an academic policeman?” one professor wondered in an essay about the “trap” he set for his students to detect their use of AI.
Restoring trust and support
This fragmented approach to AI strategy hurts students and faculty alike. Students grow resentful of not being trusted in the classroom, and struggle with variations in expectation from course to course.
For their part, faculty often have to keep up with shifting or contradicting expectations at the departmental and university-wide level, and must make decisions about how to implement a complex, ever-changing technology with limited guidance.
At the same time, the more extreme anti-AI backlash that has spurred many instructors to go back to oral exams and blue books may be inadvertently leading to the removal of differentiation strategies designed to support different learning styles, harming students who benefit from the opportunity for iteration, longer reflective periods, and asynchronous assessment.
Without a clearer overarching vision for what an institution’s application of AI should look like, it’s easy for both sides to retreat to their respective corners. Hostility and mutual distrust become the predominant emotions in the classroom.
As many instructors will tell you, this is already happening—ambiguity and a lack of direction around AI is wearing down faculty and leaving students feeling unsupported. The question now is what to do to restore the feeling of trust and support that underpins healthy classroom environments.
Defining the “why” of AI
Here’s what needs to happen: shared language and shared goals which stem from a clear vision for AI use across institutions and within classrooms. Purdue’s strategy, for instance, incorporates five pillars—Learning with AI, Learning about AI, Research AI, Using AI and Partnering in AI—which, for many institutions, could be a helpful place to start.
They include clear-cut explanations of when AI use is acceptable and what constitutes cheating, as well as frank conversations with students and faculty about the “why” around expectations of AI use. They also incorporate comprehensive education on AI literacy, on how AI works, and on the consequences of use.
The lack of cohesive AI policies on campus is, in many ways, an understandable one. Institutions are balancing a host of intersecting and competing priorities, and it’s not easy to come up with a big-picture vision for something as consequential as AI without taking many perspectives into account.
Many may also be anxious about their policies being overly restrictive or heavy-handed, or that they will unintentionally push AI on students and programs where it hurts more than it helps. These are understandable concerns. But at the pace that AI is transforming higher education, a work-in-progress goal is better than no goal at all.
Faculty members have a role to play here too, of course—and they don’t need to wait until their institution’s policy has been finalized to rebuild bridges with their students. By meaningfully integrating AI into their curriculum, faculty can be on the front lines of implementing AI in ways that are intellectually engaging for students, while also helping them make use of the technology that’s increasingly likely to play a role in their careers.
Many are already putting this into practice: Stephen Lemay of the University of West Florida, for instance, has students evaluate a range of AI outputs for a business project, weaving the technology into an existing assignment to help students understand how to critique and improve what it produces.
Collaborative rather than a combative
Purdue’s announcement is a sign that at a growing number of institutions, faculty and students won’t have to make up their own AI rules for much longer. A clearer and more proactive institutional vision can help build classroom environments that make learning about AI a collaborative experience, rather than a combative one.
The institutions that build a shared culture and language in place around AI, and who continue to listen, learn, and evolve their policies and practices over time, will find their students better prepared for life after college—not just because they know how to use AI in the workplace, but because their institution has equipped them with the critical thinking to use it well.
