For the higher education sector, 2026 is likely to be another year of grappling with the power of generative artificial intelligence to reshape research, teaching, learning and campus operations.

Those conversations have evolved since November 2022, when Open AI’s ChatGPT—capable of generating essays, images and homework answers in seconds—went mainstream. Soon after, numerous other companies launched similarly powerful large language models, such as Google’s Gemini and Anthropic’s Claude.

In 2023, many colleges and universities focused on fears that students would use AI as a tool for cheating. Even so, by 2024 more universities had begun adopting AI-powered tools, though the sector was still figuring out how best to use them; an Inside Higher Ed survey of chief technology officers from that year showed that just 9 percent said they believed higher education was prepared for AI’s rise. Despite that, tech companies and universities alike both bet big on AI in 2025.

In February, the California State University system announced a public-private partnership with Microsoft, OpenAI, Google and other tech companies as part of an effort to build an AI-ready workforce. In August, the company that owns the learning management system Canvas announced a partnership with OpenAI to integrate into the platform native AI tools and agents. And this fall, Ohio State University launched its campuswide AI fluency initiative, which requires every student to learn how to use AI tools.

Meanwhile, some investors and tech executives closed out 2025 worried that the AI bubble may soon burst.

What will all this mean for colleges and universities in 2026? Only time will tell, but Inside Higher Ed interviewed a handful of experts about what they’re watching for at the intersection of technology and higher education this year.

(These predictions have been edited for length and clarity.)

1. The future depends on what happens to the AI bubble.

Bryan Alexander, a higher education scholar, futurist and author of the new book Peak Higher Ed: How to Survive the Looming Academic Crisis

Looking ahead to 2026, a lot rests on what happens to AI in the wider world.

If AI experiences a major market correction—if the bubble pops—we could see external pressures for academia to deploy AI slacken. We might also see internal demands for AI slow, from faculty members to career services staff and governing boards. If there are also any major negative developments with the technology, like a disaster which people generally attribute to AI, or if public attitudes towards large language models sour dramatically, we might similarly see academic appetite for AI shrink.

On the other hand, if the AI sector keeps charging ahead or stabilizes, academic AI efforts will likely continue or expand. This will play out unevenly among campuses, depending on each institution’s strategic discussions, technological environment, politics and financial situation, but we can expect a range of efforts based on what we’ve seen over the past few years. Curricular implementations will consider everything from teaching about AI in individual sections to offering campuswide programs, like school-based AI literacy plans or Ohio State University’s AI Fluency initiative. Research into AI will continue, starting with the computer science field, but also in disciplines such as economics, political science, new media studies and psychology as each applies its distinct intellectual methods to the topic.

Over all, much depends on how attitudes change about AI and the academy.

It is possible that a public turning negative on AI will value colleges and universities more highly, if academics appear to be more trustworthy than a technology many view as dubious or threatening. Conversely, if social views turn more pro-AI and the currently grim perception of higher ed persists, we might see enrollment in and funding for campuses decline by the end of 2026 as people turn to a technology they prefer.

I fear that popular opinion may increasingly view the academy as too expensive, out of touch and unreliable for many reasons [and make] people turn to AI for educational needs. Some in the postsecondary world may anticipate this possibility and try to reform academia to forestall it.

2. Institutions will seek to scale AI strategies and develop ways to measure their impact.

Lindsay Wayt, senior director of business intelligence for the National Association of College and University Business Officers.

Over the last few years, we’ve seen colleges and universities approach AI with a sense of responsible optimism. They’ve been piloting tools that they’ve purchased or built in-house and they’ve been creating opportunities for faculty and staff to build their AI knowledge and skills. I believe this approach will continue across higher education as institutions work to scale AI strategies and uses to the enterprise level.

The pace of change is the biggest challenge confronting colleges and universities when it comes to fully leveraging AI. Yes, there are cost, security, privacy and environmental concerns. But most of my conversations with NACUBO members have focused on the pace of change, which exacerbates these other concerns.

As the role of AI in higher education expands, faculty, staff and administrators should expect to see what has been piloted over the last couple of years expand and improve. And as leaders continue to make sure AI use supports the institutional mission, priorities and students, we’ll see more leaders, especially business officers, looking for effective ways to measure and communicate the return on investment in AI tools and resources.

3. Higher ed should be prepared for growing AI disillusionment.

Rebecca M. Quintana, clinical associate professor at the Marsal Family School of Education at the University of Michigan. Her teaching is situated in the Designing for Innovation: Learning, Instruction and Technology concentration within Educational Studies.

Optimism remains strong and expectations are still high, though this may be beginning to shift; we may soon be entering [a period] of disillusionment as educators and institutions more seriously grapple with the costs associated with AI use, including environmental and societal impacts. At the same time, today’s AI-powered tools are still relatively underdeveloped and are likely to change rapidly in the months and years ahead. Our understanding of what AI looks like in teaching and learning contexts will almost certainly be very different even two years from now.

Faculty, students and administrators should also be prepared for a growing resistance to AI use within higher education contexts. While there may have been initial curiosity or even enthusiasm for the use of these tools, many are becoming weary of the additional challenges that AI introduces to contexts of teaching and learning.

Faculty may be observing that students are using AI in ways that do not support their learning and growth. Some are working to “resist” full adoption or uncritical acceptance of AI in their classrooms, often in deliberate and creative ways such as with voice memos and handwritten assignments. Students are also sensing that extended AI use does not align with their personal educational goals and ethical stances. Some students have shared that they have grown fatigued by the relentless focus on AI and have wished for attention to shift to other topics.

This moment presents an opportunity to foreground foundational practices, such as critical engagement with course materials, and invites broader conversations about the purposes of education and schooling.

4. To maintain their momentum, ed-tech companies will be looking to build connections between technology leaders and their campus communities.

Mark McCormack, senior director of research and insights at Educause

Institutions are going to continue to be challenged to adapt to our evolving technology and AI capabilities in the year ahead, particularly in figuring out how to balance the need for responsiveness and innovation on the one hand with the need for intentional and careful adoption and decision-making on the other.

There’s a clear North Star for the higher education technology community in navigating these challenges: cultivating connections. In 2026, technology leaders will be focused on equipping and empowering people across their institutions to realize the net benefits of technology, AI and data. This will require leaders who can educate and train users in the safe, effective adoption of these tools, while also partnering closely with academic and programmatic leaders to ensure students gain the skills they need for their educational journeys and future careers.

Faculty will remain on the front lines of AI adoption, navigating their own use while also guiding and supporting students’ use of these tools. And beyond the classroom, AI has the potential to drive administrative efficiency and more sophisticated decision-making. Across all these institutional contexts, our technology teams will have to remain connected—present and responsive, providing guidance, listening to concerns and building trust through sustained, human-centered support.

Connections at the institutional level are also going to be critical in 2026. Individual stakeholders are most effectively empowered and equipped when the institution is built on a solid foundation of shared governance and management for its technologies, AI and data.

5. Institutions will work to end system fragmentation and use AI to boost efficiency and automation across departments, platforms and offices.

Joe Abraham, CEO of Intellicampus, an ed-tech start-up focused on using generative AI to enhance the student experience.

In 2026, higher education institutions will increasingly prioritize ending the fragmentation of systems that were never designed to work together.

Advising platforms, enrollment tools, financial aid, billing and LMS data often operate in isolation, creating complexity, cost and blind spots. Institutions will need to find ways to unify data, workflows and insights without replacing existing systems. Specifically, looking into agentic orchestration and workflow automation that will enhance speed, coordination and accuracy without adding new tools for staff to learn or manage. This will ensure institutionwide impact: stronger student and faculty experiences, simpler operations, and measurable outcomes that demonstrate the value of connected, intelligent systems.