The rapid advancement and reactive implementation of artificial intelligence in higher education has cultivated a fundamental tension: How can institutions pursue open collaboration with other institutions or outside partners while also protecting the privacy of their students, faculty, and staff? The reality is a genuine trolley problem, in which pursuing one goal often means sacrificing the other. Institutional actions shape data governance, technological ethics, and what it even means to be an institute of higher education in the age of AI. Too often, these decisions default to either overreach or paralysis, the latter especially as schools adopt a misguided belief in AI as inherently neutral or benevolent.
Universities once collected limited information about their students and focused on producing knowledge. Today, they are expected to collect detailed, real-time information on students—from practice test scores to time spent on materials to how often they access class readings. All of this data is created as students move through digital platforms designed to track them. This information is increasingly viewed not just as a byproduct of the students’ education, but also as a tool to optimize the educational system itself. Because data-driven predictive AI insights might help further personalize learning, improve student retention, or identify struggling students early, universities are told that using the data is responsible and forward-looking. That same logic means that higher education institutions view not using data as falling behind.
AI is often seen as a force multiplier, a tool that simply becomes better the more data it’s fed. But data isn’t magic. It’s not inherently powerful or good. In fact, more is not always better. Data’s value—and its risk—depends entirely on how and why it’s used and who controls it. Further, AI models will more consistently generate useful information if given focused, higher-quality data, rather than entire datasets that haven’t been cleaned or filtered. In this sense, data governance is AI governance. Decisions about how data is collected, structured, protected, and shared shape the kinds of systems universities enable—and whether those systems serve institutional values or undermine them.
“Decisions about how data is collected, structured, protected, and shared shape the kinds of systems universities enable—and whether those systems serve institutional values or undermine them.”
There are undeniable opportunities in implementing AI to bolster learning: greater personalization, real-time feedback, and improved decision-making tailored to diverse student needs. But those potential benefits have been used to justify an expansive appetite for student data, often without clear boundaries or sufficient safeguards. AI systems are supposed to depend on large volumes of standardized, high-quality data, but that technical requirement is frequently misinterpreted as a license to collect data across all aspects of student life. In doing so, campuses and classrooms become sites of continuous surveillance, monitoring not only academic outcomes but behavioral cues and patterns of engagement, all in the potential service of unclear educational goals. The shift isn’t just pedagogical, it’s structural—reorienting power from faculty and administrators to opaque systems built by private vendors.
Too often, data sharing is cast as inherently good and data hoarding as inherently bad. But institutions should be grappling with how to hold their students’ data, with whom to share it, and for what purpose they are holding it in the first place. The kind of data sharing that could truly strengthen higher education—between departments, across institutions, or even making data open source—remains rare and under-resourced. It’s also hard to implement within current limited conceptions of data governance. To move forward, institutions need to redefine what sharing means, not as an open invitation for institutions to extract students’ information but as a practice of collaborative stewardship that is intentional, centered around privacy, and aligned with their missions.
Most institutions lack the internal infrastructure to collect and use students’ information efficiently, so they turn to third-party vendors offering platforms that promise both insights and safety. What results is the following: Instead of building internal capacity to use data in ways that align with academic values, institutions hand over key functions and student data to external platforms. These platforms offer polished tools and promise greater efficiency, but in practice, they seize control and obscure the educational institution’s visibility. Data flows upward into proprietary systems where institutions may have limited access and limited ability to adapt tools to evolving needs. Meanwhile, the potential for data to support core educational goals—enhancing instruction, informing research, strengthening student well-being—is under-explored.
“To truly use AI to improve their educational goals, institutions must reject the false binary of data hoarding versus sharing.”
To truly use AI to improve their educational goals, institutions must reject the false binary of data hoarding versus sharing. They must instead build structures that prioritize ethical collaboration over extractive use. AI’s role in higher education is not inevitable; it is a choice that universities must make with intention, vision, and care.
