University leaders are thinking a lot about AI. Some institutions are purchasing site licenses, others forming task forces and others are drafting policies focused on academic honesty. Meanwhile, students are quietly bearing a cost that few are tracking: between $1,200 and $1,800 over four years in AI tool subscriptions that fragmented and unenforceable institutional policies have made necessary.
Here’s what a typical student experience looks like. Freshman fall semester: The composition professor bans ChatGPT even though the university has a site license. The biology lab recommends NotebookLM for research synthesis. The math professor encourages Wolfram|Alpha Pro Premium at $8.25 per month. Spring semester brings a different writing professor, who requires Grammarly Pro at $12 monthly, while the computer science intro professor suggests GitHub Copilot Pro for $10 monthly (though it’s worth noting here—props to GitHub Copilot—that verified students may be eligible for free access to the Pro plan). Meanwhile, the research methods professor advises students to “use AI responsibly” without defining what that means.
As students progress, the costs compound. Statistics courses need IBM SPSS Statistics with AI features or Jupyter with premium compute, such as through a Google CoLab Pro subscription ($9.99 per month). Marketing classes require Canva Pro for design projects at $15 monthly. Capstone courses recommend Claude Pro at $20 monthly, or premium versions of research tools like Consensus or Elicit running anywhere from $10 to more than $40 per month. Different courses equal different tools, and the subscription stack grows. The money matters—$1,200 to $1,800 is significant for students already stretching every dollar. But the financial burden reveals something more troubling about how policy fragmentation or policy stall is undermining educational equity and mission. The problem runs deeper than institutional inaction.
Without coordination, universities face two unsatisfying options. Option one: Buy nothing centrally. Students bear the full cost—potentially $4 million to $7 million in aggregate per year for a 15,000-student institution—creating massive equity gaps and graduates unprepared for AI-integrated careers. Option two: Attempt institutional licensing. But this means more than purchasing a single large language model. Writing disciplines might work with ChatGPT or Claude. But other disciplines might need GitHub Copilot, Canva Pro, AI-enhanced modeling platforms, Consensus, Elicit, AI features in SPSS or premium Jupyter compute. There are thousands of AI platforms out there.
A truly comprehensive strategy for a large university could exceed $2 million annually—with no guarantee of faculty adoption or pedagogical integration. So even with an investment, without consensus or agreement, students might still experience this AI tax. Some institutions have the financial capacity to invest in both comprehensive licensing and faculty development. But most universities facing enrollment pressures and constrained budgets cannot afford coordinated AI strategy at this scale. The result is policy paralysis while students continue paying out of pocket. Some institutions have tried a middle path, purchasing site licenses for tools like ChatGPT Edu or Claude for Education. But without cross-functional coordination, these investments often miss their mark.
The fundamental barrier is really a structural one. Procurement authority typically resides with the chief information officer, while pedagogical decisions belong to the provost and faculty. The information technology office selects tools based on security, scalability, cost and vendor relationships and reliability. Faculty need tools based on disciplinary fit, learning outcomes and individual professional preparation. These criteria rarely align. If an institution does purchase something, it may sit underutilized while students continue paying for what they actually need or what faculty require or prefer.
This creates the unintentional equity crisis: Two students in the same capstone course may face dramatically different access. Student A, working 20 hours weekly and Pell Grant eligible, cannot afford premium subscriptions. She uses free versions with severe limitations and usage caps—and when those caps hit midassignment, her work stalls. Student B, with family financial support, maintains premium subscriptions for every required tool with unlimited usage and priority access. Student B’s AI-enhanced work earns higher grades not because of deeper learning, but because of subscription access. Academic advantages compound over time and may continue past college and into the career.
Universities have created an unintentional AI tax here on students that exacerbates grade inflation, does not ensure learning of content and is costing students. Universities have always operated on a principle of equal access to essential learning resources. AI has become essential to academic work, yet access remains unequal.
The academic commons is breaking down. The coordination gap is structural—and fixable. Technology teams focus on infrastructure and security. Academic affairs manages curriculum and pedagogy. Student success addresses traditional access barriers. Financial aid handles emergency requests for support case by case. In practice, the CIO and provost rarely will coordinate at the operational level, where these decisions actually get made.
The employability implications compound the equity concerns. One survey found that 26 percent of hiring managers now consider AI fluency a baseline requirement, with 35 percent actively looking for AI experience on résumés. Students graduating without systematic AI literacy preparation face workforce disadvantages that mirror the educational inequities they experienced, disadvantages that may extend into career outcomes and lifetime earnings.
The real question isn’t “What should we buy?” Instead, universities need to ask themselves, “What is AI fluency and how do we know if students are getting it?” Then, “How do we make strategic decisions about what gets institutional investment—not just licenses but also faculty buy-in and development—versus what students purchase?” That requires executive-level strategic coordination that bridges IT and academic affairs, something most universities lack.
The conversations are happening in separate silos when they need to converge. Until they do, universities will continue creating hidden taxes for students while wondering why AI investments aren’t delivering promised educational transformation. Students caught in this gap might not even be aware it is happening and not have the language or platform to name it.
Higher education’s democratic mission requires equal access to essential learning tools. AI has become essential. Access remains unequal. Costs are passed to the students. The longer institutions delay action, the wider these gaps grow.
Kenneth Sumner is founder and principal of Beacon Higher Education, which provides AI governance consulting for colleges and universities. He previously served as provost at Manhattan University and has held associate provost and dean roles at Montclair State University. He holds advanced AI strategy and design and innovation certifications from the Wharton School at the University of Pennsylvania and Stanford University School of Business.
