Artificial intelligence is transforming how colleges and universities teach, learn and conduct research, but the conversation often overlooks a fundamental question: Can every student actually access the technology needed to participate? As institutions adopt AI-powered applications and increasingly resource-intensive software, ensuring equitable access has become as important as the technology itself.
I spoke with Keegan Moodley, vice president of customer experience at AppsAnywhere, about the challenges colleges are facing as they work to expand access to academic software, support increasingly diverse learning environments and make technology investments that improve both student outcomes and institutional efficiency. Working closely with colleges and universities every day, Keegan has gained a front-row view of the strategies institutions are using to improve digital access while making smarter technology decisions.
Q: How can colleges ensure AI initiatives benefit all students, not just those with access to BYOD high-powered devices or campus computer labs?
A: One of the biggest risks as higher education adopts AI is unintentionally widening the digital divide. AI has enormous potential to personalize learning, improve student support and prepare students for the workplace, but those benefits depend on students being able to access the right tools regardless of the device they own or where they choose to study.
Access also starts with awareness. Students don’t always know what software is available to them, and AI adds another layer of uncertainty around which tools are institutionally approved and appropriate for academic work. A centralized catalog of approved applications gives students a clear place to start and confidence that the technology they’re using is supported by their institution.
Colleges also need to move beyond relying primarily on physical computer labs for specialized software. Students are learning across campus and increasingly remotely, often on their own devices. They should be able to access the applications they need without expensive hardware or having to understand how the technology is being delivered behind the scenes.
That requires choosing the right delivery method for the student, device and application. Some software can run locally, while more demanding applications may require virtual infrastructure. Ideally, those decisions happen automatically via a centralized portal without the students even realizing which method is being used.
AI can help create a more equitable student experience, but only when access is part of the strategy from the beginning. Making software easier to find and access gives more students the opportunity to benefit from the technology their institution provides.
Q: With budgets under increasing pressure, how can technology leaders balance expanding access to software with making responsible investment decisions?
A: Higher education technology leaders are being asked to provide more digital capability at a time when budgets are increasingly constrained. That makes understanding how resources are being used especially important.
Resource-intensive, specialized software is a good example. For years, virtual desktop infrastructure has given students access to demanding applications regardless of their device, and it continues to serve an important purpose. But it’s also an expensive way to deliver software and isn’t necessary for every application or every student.
Student devices are far more capable than they were five or 10 years ago. If an application can run securely and effectively on a student’s laptop, there’s little reason to consume costly virtual infrastructure to deliver it. The key is being able to make that decision dynamically rather than asking students or IT teams to determine the best delivery method themselves.
An intelligent approach to software delivery can assess the device, application and user context and select the appropriate option. Software can run locally when possible, while virtual infrastructure remains available for applications that require specialist hardware, greater computing power or controlled environments.
This allows institutions to reserve their most expensive resources for the students and applications that actually need them while making access simpler for everyone else. As AI places additional demands on institutional infrastructure, being more deliberate about where computing resources are used will become even more important.
Q: What role does software-usage data play in helping institutions understand whether their technology investments are actually supporting student success?
A: Software-usage data gives technology leaders a much clearer picture of what’s actually happening. You can’t attribute student success to a single application, but you can see whether students are accessing the tools they need, which courses rely most heavily on specialized software and whether expensive licenses and computing resources are actually being used.
That visibility is especially important when institutions are under pressure to do more with less. A managed computer can cost close to $2,000 annually, yet utilization often remains below 20 percent, while software usage can fall below 5 percent. When you start looking at usage across an entire institution, a significant gap emerges between what’s being provided and what students are actually using.
That’s where the data becomes really valuable. By analyzing usage patterns across devices, applications and learning spaces, institutions can identify underused software licenses, unnecessary hardware and other areas where resources could be better allocated. We’ve seen that level of analysis uncover potential savings of $1 million to $3 million annually at an individual institution. More importantly, it gives IT teams the evidence they need to decide where they can reduce spending without limiting student access.
That same data can help inform decisions about the physical campus. If more students are using their own devices or accessing specialized applications remotely, institutions need to understand what that means for computer labs and other learning spaces. On the other hand, strong demand for certain labs or specialized facilities gives them evidence to continue investing where those resources are genuinely needed.
As AI changes how students learn and interact with technology, having that visibility will become even more valuable. Understanding how students are actually using technology gives institutions a stronger basis for deciding where to invest, where to pull back and how to make sure limited resources are supporting the way students really learn.
