University leaders have given a lot of thought to artificial intelligence. Some institutions are purchasing website licenses, others are forming working groups, and still others are drafting policies focused on academic integrity. Meanwhile, students are silently bearing costs that few track: Subscriptions to artificial intelligence tools cost between $1,200 and $1,800 over four years, and fragmented and unenforceable institutional policies make these tools necessary.
This is a typical student experience. Fall semester for freshmen: Composition professor bans ChatGPT, despite university having website license. Biological laboratories recommend using NotebookLM for research synthesis. The math professor encouraged Wolfram|Alpha Pro Premium for $8.25 per month. The spring semester brought a different writing professor who asked for $12 a month for Grammarly Pro, while an introductory computer science professor suggested $10 a month for GitHub Copilot Pro (although it’s worth noting here that GitHub Copilot is supported – verified students may qualify for the Pro plan for free). Meanwhile, a research methods professor advised students to “use AI responsibly” but did not define what that meant.
As students progress, costs increase. Statistics courses require IBM SPSS Statistics with AI capabilities or Jupyter with advanced computing capabilities, such as through a Google CoLab Pro subscription ($9.99 per month). The marketing course requires Canva Pro to complete design projects, which costs $15 per month. Capstone courses recommend Claude Pro, which costs $20 per month, or premium versions of research tools such as Consensus or Elicit, which range from $10 to over $40 per month. Different courses equal different tools, and the subscription stack keeps growing. Money matters—$1,200 to $1,800 can mean a lot to a student who has already spent every dollar. But the financial burden reveals something more troubling about how policy fragmentation or policy stagnation undermines educational equity and mission. The problem is more serious than institutional inaction.
Without coordination, universities are faced with two unsatisfactory options. Option 1: Buy everything together. Students bear the full cost—which can total $4 million to $7 million per year for an institution with 15,000 students—creating a large equity gap and leaving graduates unprepared for AI-integrated careers. Option two: Try institutional licensing. But this doesn’t just mean buying a single large language model. Writing disciplines may apply to ChatGPT or Claude. But other disciplines may require GitHub Copilot, Canva Pro, the AI-enhanced modeling platform, Consensus, Elicit, AI capabilities in SPSS, or advanced Jupyter calculations. There are thousands of AI platforms out there.
For a large university, a truly comprehensive strategy might cost more than $2 million per year, but there would be no guarantee of faculty adoption or instructional integration. So even with investment, without consensus or agreement, students may still experience this AI tax. Some institutions have the financial resources to invest in comprehensive licensing and faculty development. But most universities, facing enrollment pressures and limited budgets, cannot afford a coordinated AI strategy of this scale. The result is policy paralysis while students continue to pay 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 the mark.
The fundamental barriers are actually structural barriers. Purchasing authority typically rests with the chief information officer, while instructional decisions rest with the provost and faculty. The Information Technology Office selects tools based on security, scalability, cost, and vendor relationships and reliability. Teachers need tools based on subject fit, learning outcomes, and personal professional preparation. These standards are rarely consistent. If an institution does purchase something, it may not be fully utilized, while students continue to pay for something they actually need or something the faculty requires or prefers.
This creates an unintentional equity crisis: two students in the same capstone course may face dramatically different access. Student A works 20 hours a week, is Pell Grant-eligible, and cannot afford the premium subscription. She uses the free version with strict restrictions and usage caps, and when those caps hit mid-allocation, her work stalls. Student B, with family financial support, gets a premium subscription for each required tool, with unlimited use and priority access. Student B’s AI-enhanced assignment received a higher grade not because of deep learning, but because of subscription access. Academic advantages compound over time and may carry over into a career after college.
Universities are inadvertently imposing an AI tax on students, fueling grade inflation, failing to ensure content learning, and costing students. The University has always adhered to the principle of equal access to essential learning resources. Artificial intelligence has become an important part of academic work, but access remains unequal.
Scholarly Commons is collapsing. Coordination gaps are structural and can be repaired. The technical team focuses on infrastructure and security. Academic Affairs Management Curriculum and Pedagogy. Student Success addresses traditional barriers to entry. Financial Aid handles requests for emergency support on a case-by-case basis. In practice, CIOs and provosts rarely coordinate at the operational level where these decisions are actually made.
The impact on employability adds to equity concerns. One survey found that 26% of hiring managers now view AI proficiency as a basic requirement, with 35% actively looking for AI experience on their resumes. Students who are not systematically prepared with AI literacy face workforce disadvantages upon graduation that reflect the educational inequalities they experience, and these disadvantages may extend to career outcomes and lifetime earnings.
The real question is not, “What should we buy?” Instead, universities need to ask themselves, “What is AI fluency, and how do we know if students have mastered it?” Then, “How do we make strategic decisions about what gets institutional investment (not just licenses, but faculty support and development), and what students buy?” This requires executive-level strategic alignment that bridges information technology and academic affairs, something most universities lack.
When conversations need to converge, they happen in separate silos. Until then, universities will continue to create hidden taxes for students while wondering why investments in AI are not delivering the promised educational transformation. Students caught in this gap may not even realize it’s happening and don’t have the language or platform to name it.
The democratic mission of higher education requires equal access to essential learning tools. Artificial intelligence has become crucial. Access remains unequal. The cost is passed on to students. The longer agencies delay action, the wider these gaps will become.
