For the higher education sector, 2026 could be the year we grapple with the power of generative artificial intelligence to reshape research, teaching, learning, and campus operations.
These conversations have evolved since November 2022, when Open AI's ChatGPT, which can generate essays, images, and homework answers in seconds, went mainstream. Soon after, many other companies launched similarly powerful large-scale language models, including Google's Gemini and Anthropic's Claude.
In 2023, many universities focused on concerns that students would use AI as a cheating tool. Still, by 2024, more universities had started implementing AI-powered tools, but the field was still figuring out how best to leverage AI. of Inside higher education In a survey of chief technology officers that year, only 9% said they believed higher education was prepared for the rise of AI. Nevertheless, technology companies and universities alike are making big bets on AI in 2025.
In February, the California State University System announced public-private partnerships with Microsoft, OpenAI, Google, and other technology companies as part of an effort to build an AI-enabled workforce. In August, the company that owns learning management system Canvas announced a partnership with OpenAI to integrate into the platform's native AI tools and agents. And this fall, Ohio State University launched a campus-wide AI fluency initiative, requiring all students to learn how to use AI tools.
Meanwhile, some investors and tech executives left 2025 worried that the AI bubble could soon burst.
What does this mean for universities in 2026? Only time will tell. Inside higher education We spoke to several experts about what they're focused on at the intersection of technology and higher education this year.
(These predictions have been edited for length and clarity.)
- The future depends on what happens to the AI bubble.
Brian Alexander, higher education scholar, futurist, author of new book Higher education peaks: How to survive the looming academic crisis
As we head into 2026, a lot depends on what happens to AI in the wider world.
If AI experiences a major correction in the market, i.e. if the bubble bursts, external pressure on academia to adopt AI may weaken. It is also likely that internal demand for AI will slow, from faculty to career services staff to boards of directors. A major negative development in technology, such as a disaster that people generally attribute to AI, or a dramatic deterioration in public attitudes toward large-scale language models could similarly dampen academic enthusiasm for AI.
On the other hand, academic AI efforts are likely to continue or expand as the AI sector continues to advance or stabilize. This will play out unevenly across campuses depending on each university's strategic discussions, technological environment, and political and financial circumstances, but based on what we've seen over the past few years, we can expect a variety of initiatives. Curriculum implementation considers everything from educating individual sections on AI to delivering campus-wide programs such as school-based AI Literacy Plans and The Ohio State University's AI Fluency Initiative. Research in AI begins in the field of computer science and continues in fields such as economics, political science, new media studies, and psychology, each of which will apply their own intellectual methods to the subject.
Overall, a lot depends on how attitudes towards AI and the academy change.
An AI-negative public may rate universities more highly if academics appear to be more trustworthy than technologies that many people view as questionable or threatening. Conversely, if societal views shift toward AI and current harsh perceptions of higher education persist, campus enrollment and campus funding could decline by the end of 2026 as people turn to their preferred technologies.
I fear that public opinion will increasingly grow that the academy is too expensive, out of touch, and unreliable for a number of reasons. [and make] People turn to AI for their educational needs. Some in the postsecondary world may anticipate this possibility and seek to reform academia to prevent it.
- Educational institutions look to expand their AI strategies and develop ways to measure their impact.
Lindsey Waite is senior director of business intelligence at the National Association of University Business Officers.
In recent years, we have seen universities approach AI with responsible optimism. They are piloting tools they have purchased or built in-house, creating opportunities for faculty to build AI knowledge and skills. I believe this approach will continue across higher education as institutions work to extend their AI strategies and use to the enterprise level.
The speed of change is the biggest challenge for universities when it comes to making the most of AI. Yes, there are cost, security, privacy, and environmental concerns. But most conversations with NACUBO members focus on the pace of change, which compounds other concerns.
As the role of AI in higher education expands, faculty, staff, and administrators should expect what has been piloted over the past few years to expand and improve. And as leaders continue to ensure that the use of AI supports their organization's mission, priorities, and students, more leaders, especially executives, will seek effective ways to measure and communicate the return on investment in AI tools and resources.
- Higher education must prepare for growing disillusionment with AI.
Rebecca M. Quintana, Clinical Associate Professor, Marsal School of Family Education, University of Michigan; Her teaching focuses on “Design for Innovation: Learning, Instruction, and Technology” within educational research.
rebecca quintana
Optimism remains strong and expectations remain high, but things may be starting to change. Might come in soon [a period] Disillusionment is growing as educators and institutions grapple with the costs associated with using AI, including its environmental and social impacts. At the same time, today's AI-powered tools are still relatively underdeveloped and are likely to change rapidly in the coming months and years. Our understanding of what AI in teaching and learning looks like will almost certainly be very different even two years from now.
Faculty, students, and administrators should also prepare for increased resistance to the use of AI in higher education. While there may have been some initial curiosity or even enthusiasm for using these tools, many are becoming weary of the new challenges that AI brings to teaching and learning situations.
Instructors may find that their students are using AI in ways that don't support their learning and growth. Some are trying to “resist” the full introduction or uncritical acceptance of AI in the classroom. They are often approached in intentional and creative ways, such as voice notes or handwritten assignments. Students also feel that the widespread use of AI is inconsistent with their personal educational goals and ethical stances. Some students say they are tired of the relentless focus on AI and want their attention shifted to other topics.
This moment provides an opportunity to bring fundamental practices to the fore, such as critical engagement with course materials, and encourages broader conversations about the purpose of education and schooling.
- To maintain momentum, edtech companies will seek to forge connections with technology leaders and campus communities.
Mark McCormack, Senior Director of Research and Insights, Educause
mark mccormack
In the year ahead, educational institutions will continue to be challenged to adapt to evolving technology and AI capabilities. Specifically, finding ways to balance the need for responsiveness and innovation on the one hand with the need for deliberate and deliberate adoption and decision-making on the other.
In navigating these challenges, there is a clear north star for the higher education technology community. It's about fostering connections. In 2026, technology leaders will focus on equipping and empowering people across their organizations to realize the ultimate benefits of technology, AI, and data. This will require leaders who can educate and train users to safely and effectively deploy these tools, while also working closely with academic and program leaders to ensure students acquire the skills they need for their educational journeys and future careers.
Faculty remain at the forefront of AI adoption, guiding and supporting students' use of these tools while navigating their own use. And AI has the potential to drive administrative efficiency and better decision-making beyond the classroom. In all of these organizational situations, our technology teams must stay connected, always present, provide guidance, listen to concerns, and build trust through sustained, human-centered support.
In 2026, connections at the organizational level will also be important. Individual stakeholders are most effectively empowered and equipped when organizations are built on a strong foundation of shared governance and management of technology, AI, and data.
- Educational institutions will strive to end system fragmentation and leverage AI to increase efficiency and automation across departments, platforms, and offices.
Joe Abraham, CEO of Intellicampus, an education technology startup focused on improving the student experience using generative AI.
In 2026, higher education institutions will increasingly prioritize ending the fragmentation of systems that were not designed to work together.
Advice platforms, enrollment tools, financial aid, billing, and LMS data often operate in isolation, creating complexity, cost, and blind spots. Educational institutions need to find ways to integrate data, workflows, and insights without replacing existing systems. Specifically, we're looking at agent orchestration and workflow automation to improve speed, coordination, and accuracy without adding new tools for staff to learn or manage. This can have a positive impact across your organization, including enhanced student and faculty experiences, simplified operations, and measurable outcomes that demonstrate the value of connected, intelligent systems.
