AI Leadership in Higher Education Starts With a More Coherent Student Experience

One way of understanding the last several years of work on digital teaching and learning is as a transition from fragmented to coherent. University departments, institution-wide project teams, and sector-wide collaborations have been building up capacity to ensure evidence-based teaching practices are enabled by technology in ways that benefit every learner. This transition from fragmented to coherent has been necessary to bring together the learnings from scattered implementation, promising experiments, limited pilot programs, unsynthesized data, and uneven access.

Today, the fragmented experience students have had with digital learning more broadly is being echoed by the way AI in particular is influencing higher education. For example, in Student Research Into How Students and Faculty Use AI: Insights for Teaching and Learning, which was developed by a group of Every Learner Everywhere student interns, one prominent finding is how conscious students are that their experience with AI varies greatly. They see how much it depends on individual faculty, what their major is, or their own personal financial resources or educational background. They are not experiencing a college education that responds to the AI transformation in a coherent way.

The higher education sector could wait 20 years for that arc from fragmented to coherent to resolve itself, but that is much more dangerous with AI than it was with adaptive courseware or other previous digital learning technologies. AI is moving quickly from tool to infrastructure, and it is already influencing course design, student support, assessment, and institutional operations. Most critically, AI is restructuring how knowledge is produced, demonstrated, assessed, and valued.

AI adoption has also outpaced any realistic expectation of banning it from the educational experience. Every month brings a fresh survey of a growing percentage of students, faculty, and staff all using AI in their daily work, regardless of any policy to limit it. Every Learner’s position on AI is fundamentally the same as it always has been for digital learning — that it has tremendous potential to help close achievement gaps and improve learning for all students, but that this potential will not be realized automatically. It requires pairing thoughtful deployments of technology in support of evidence-based teaching practices. Every Learner is working with partners and institutions that take an AI posture of “explore, not ignore,” and we particularly emphasize doing so in ways that are anchored in accessibility and evidence.

The flood of surveys also show that the workplaces students will graduate into increasingly have AI use as part of everyday knowledge work. For example, a National Association of Colleges and Employers 2026 survey of employers shows that the share of entry-level jobs requiring AI skills leapt from 13 to 35 percent in one year. A survey by Strada Education Foundation shows that AI is decreasing the time employees in entry-level roles spend on “routine” tasks and increasing the time they spend on “analytical and judgment-based responsibilities.” Many colleges and universities, as a result, are considering AI literacies as a core graduation outcome, which in turn creates a fundamental capacity-building need for faculty that one-off workshops will not move.

For all the reasons above, Every Learner believes AI presents higher education with a defining leadership moment. The future legitimacy of colleges and universities rests on getting the AI transition right for all students, not just those who happen to take courses that are part of pilot programs or taught by individual faculty who are AI curious. This moment will impact every student, so this moment asks higher education leaders to make values-based decisions at the same time they make technical decisions.

Put another way, the AI transformation does not simply create the need to select, license, and deploy particular tools. Whether conscious of it or not, colleges and universities are engaged in a project that goes to the core of their mission, strategy, and governance. This moment calls for coherent, coordinated leadership.

Moving from fragmented to coordinated AI adoption

You can observe fragmentation in the AI student experience in several ways:

• Conflicting policies: Syllabi, departments, and teaching and learning centers publish AI rules that contradict each other, leaving students to navigate a course-by-course policy maze.
• Shadow tools: Individual units sign their own contracts or use free AI tools at scale without central review, vetting for accessibility, cybersecurity controls, or data governance.
• Uneven AI literacies: Some faculty get intensive AI support while others get none, so students’ experience with AI swings dramatically depending on who they take courses with.
• No common metrics: Different pilots define “success” differently (time saved, grades, satisfaction) with no coordinated learning about what works for whom.

Institutional leaders must make AI-related decisions with an agreed-upon north star in sight. Ideally, this is clearly articulated in an institution-level AI statement that ties AI use to the mission, institutional goals, and student success.

Institutional leaders should strive for consistent student experiences while respecting faculty autonomy and disciplinary variety. Schools, departments, and individual faculty benefit from the opportunity to adapt common institutional frameworks into discipline- and role-specific guidance. (One example would be The Faculty Development and Gen AI Playbook Every Learner produced with network partner, The Online Learning Consortium. We also have a growing catalog of self-paced professional development courses on implementing AI for student success.) Without that leadership, faculty are left to develop policies and practices that students experience as contradictory.

The history of digital learning more broadly tells us that higher education must bring many stakeholders into the work of coordinating its response to AI:

• Shared decision-making forums: Faculty, staff, and students are voting members on AI committees, not just implementers brought in after decisions are made.
• Feedback channels: Non-teaching staff — sometimes from unexpected corners of the institution — are often closest to the pain points students experience and can surface implementation risks early, such as where AI tools create new accessibility barriers or workload challenges.
• Translators and integrators: Staff in centers for teaching and learning, academic technology, advising, and institutional research translate high-level AI vision into workflows, training, and supports that faculty and students actually experience.

The principles of cross-functional collaboration must be used to identify that north star. An AI steering group should include academic affairs, student affairs, IT, disability services, faculty, students, and potentially more stakeholders. Cross-functional collaboration ensures the principles, pilots, implementation, and communication related to AI account for every learner.

In pursuit of coherence

By no means is Every Learner arguing for a headlong rush into adopting AI. The history of fragmented deployment of digital learning stemmed in part from legitimate questions about if, why, when, and how. Likewise, there are now legitimate questions about if, why, when, and how to pursue the potential benefits of AI in teaching and learning.

But the urgency of those questions argues for more leadership in pursuit of coherence, not less. The AI decisions being made now — including the decision to wait and see — are mission-influencing strategic decisions about who higher education serves well and who gets left behind.