Tech Outlook 2026: What Higher Ed Tech Leaders Expect this Year

• By Rhea Kelly
• 01/29/26

In an open call last month, we asked higher education technology leaders for their predictions on how the tech landscape will change for colleges and universities in the coming year. Not surprisingly, artificial intelligence looms large on the horizon — but advancements in ed tech, data integration, and workforce readiness also remain key topics. Here's what respondents told us.

Artificial Intelligence Will Go Beyond the Pilot Phase

"Vendors are rapidly embedding AI into almost every layer of higher education software. For institutions, the most immediate and pragmatic value is in AI as an augmentation tool: drafting and summarizing documents, analyzing long reports and contracts, supporting grant development, triaging routine student questions, and powering early alert systems that surface at-risk students sooner and route cases more efficiently. On the academic side, the 'cat and mouse' dynamic will continue: Students will keep using AI to assist with assignments, and faculty will continue to refine detection and integrity practices. However, the trend this year should be toward reframing AI as a literate, bounded tool — similar to how calculators and spellcheckers were eventually normalized — by redesigning assignments, clarifying permitted use, and explicitly teaching prompt crafting, verification, and ethical use. Strategically, institutions should expect to invest in faculty and staff development so AI augments work rather than simply adding a new compliance burden." — Nick Swayne, president, North Idaho College

"A major AI topic in education will be determining which elements of educational context should be shared with AI systems, what must remain private, and how institutions can enforce these boundaries. As AI tools become more capable and more deeply woven into instructional workflows, institutions will increasingly focus on building comprehensive AI strategies that encourage innovation while maintaining strong oversight. These strategies will define governance structures, compliance expectations, and evaluation processes to ensure that AI adoption aligns with institutional values, legal requirements, and student protections. Ultimately, AI in education will evolve from isolated experiments to coordinated, policy-guided ecosystems, where the value of AI is balanced with the responsibility to safeguard learner information and uphold trust." — Curtiss Barnes, CEO, 1EdTech

"By 2026, higher education will be operating in a multi-AI-model world. As foundation models reach greater parity in general performance, differentiation will increasingly come from specialization — models optimized for coding, image generation, voice, research workflows, or domain-specific reasoning. At the same time, major cloud providers are already incorporating AI capabilities into their existing EDU licenses, thereby lowering barriers to entry and accelerating adoption. This will drive rapid model sprawl. Faculty, staff, and researchers will move between models and tools based on task, cost, data access, and integration needs, especially as technologies like Model Context Protocol (MCP), purpose-built connectors, and multi-model applications make it easier to combine models with institutional data and workflows. One of the key lessons learned from research and higher education's cloud adoption is that waiting too long to plan for multiple services creates governance, cost, and visibility challenges that are difficult to unwind later. Institutions underestimated multi-cloud complexity, and many are still catching up. AI is at a similar inflection point. 2026 represents a narrowing window for institutions to proactively establish governance, access controls, cost management, and visibility across multiple AI models. Those that act early will enable innovation while maintaining institutional oversight."  — Sean O'Brien, Associate Vice President for NET+ Cloud Services, Internet2

"Institutions will start thinking realistically about where AI fits and does not fit in the operation." — William Vencill, associate vice president of instruction, University of Georgia

"How AI tools will impact teaching, advising, or student support in 2026:

• AI will increasingly automate routine advising tasks (scheduling, degree audits, nudges), allowing staff to focus on complex student needs.
• Faculty adoption of AI-supported course design tools will expand, helping streamline content creation and personalize learning pathways.
• AI-powered early-alert systems will play a larger role in identifying at-risk students earlier, improving retention strategies.
• Institutions will use AI to enhance tutoring and writing support while maintaining strong guardrails to prevent over-reliance and academic integrity issues.

"All this being said, schools continue to approach student use of AI very conservatively and create barriers in the form of no use or anti-cheating policies/practices that ultimately compromise a core value prop of colleges: teaching students to be both AI literate and have strong human and technical skills to differentiate themselves in an evolving and disrupted workforce." — Charity Stutzman, senior director of higher ed strategy, Vector Solutions

"In 2026, the AI conversation in education will shift from experimentation to accountability, and that's a good thing. Institutions will need to focus on governance, including transparency, vendor selection and management, ethics, and academic integrity, while also showing what has actually improved. The most credible use cases will be the ones that take real work off educators' plates, speed up and strengthen feedback for learners, and lead to measurable gains in teaching and learning outcomes. The strongest examples of AI use won't necessarily be the campuses running the flashiest pilots, but those that can demonstrate responsible and broad, consistent adoption aligned with real outcomes." — Ryan Lufkin, VP global academic strategy, Instructure

"Much of the AI conversation in higher education has been about chatbots and content generation. That's going to change in 2026 as institutions confront a more complex challenge: the infrastructure demands of AI-enabled research and instruction. Research universities are already learning that meaningful AI work takes more than software subscriptions. It takes serious network capacity, rethinking data center strategies, and access to compute resources that most campuses simply don't have. The institutions that figure this out will be those with the vision to move past a campus-by-campus approach and embrace AI infrastructure as a shared regional and national resource. I'm hopeful that 2026 brings more collaboration on AI infrastructure: reducing duplicated investments and opening doors for smaller colleges and universities that aren't going to build their own GPU clusters. But I'm also realistic; without intentional coordination, we'll watch a capability gap widen between institutions that can afford to build their own infrastructure and those that cannot. The research and higher education community has been building shared infrastructure for decades. We know how to do this. The question for 2026 isn't whether AI transforms higher education. It's whether we seize the opportunity to apply our experience building shared infrastructure to collectively fulfill the AI-enabled education, research, and service missions of every university." — James Deaton, vice president of network services, Internet2  

"2026 will mark the arrival of campuses that genuinely feel AI-enabled. Automation will take on a meaningful share of administrative complexity, letting staff focus more on mentoring, connection, and community building. Students will begin to experience proactive digital support that adapts as their goals change, creating a more personal and intuitive path through college. However, this transition will play out unevenly. Institutions with strong technology foundations will move quickly, and may be able to complete major shifts within a year or two. Smaller or more traditional campuses will face pressure to rethink their models, ultimately expanding short term or continuous learning options. The broader AI conversation will settle into what actually works, as hype gives way to real outcomes. Data transparency and security will stay front and center, and campuses that handle this well will earn student trust. The gap between early adopters and everyone else will widen, and there will be real financial implications for waiting, but also real opportunities for schools willing to rethink how they design and deliver the student experience. — Mike Wulff, chief product & technology officer, Ellucian

"The institutions that win with AI will be the ones that use it across the entire student journey — not just in the classroom. The education sector's AI debate is stuck in a false binary: Adopt AI or preserve traditional teaching. In 2026, leaders who reject that framing will gain a decisive advantage. The data is already pointing in that direction: 61% of faculty have used AI in teaching, and 86% expect to use it more in the future. AI can flag struggling students, generate personalized practice problems, and adapt content …. But the biggest gains won't come from classroom applications alone. Universities are also using AI to help prospective students discover the right programs, streamline enrollment processes, and identify at-risk students before they drop out. The institutions that deploy AI across discovery, recruitment, enrollment, and retention will see compounding advantages: higher conversion rates, better student fit, and improved completion rates. In 2026, the institutions that pair AI with human expertise — across the entire student lifecycle, not just teaching — will pull ahead. The ones that limit AI to the classroom will miss the larger opportunity." — Kees Bol, CEO, 2U

"In 2026, newly built systems will give individual learners more agency over how they access, afford, and navigate higher education. AI can help unbundle rigid pathways, personalize preparation, and reduce friction at every step — but only if we build intentionally with that purpose in mind. If we truly want education to unlock opportunity, we have to design tools that put power in the hands of learners, not just institutions." — Jason Levin, executive director, WGU Labs

"The conversation around AI has moved beyond whether institutions should engage with it, and toward how it can be applied responsibly to advance learning. Too often, the dialogue swings between extremes — framing AI as either an existential threat to teaching or a silver bullet for improving learning outcomes. The reality is far more nuanced. When AI is applied to further learning within a robust learning design that keeps humans at the center, it expands access, strengthens student agency, and frees instructors to focus on their students. In 2026 and beyond, innovative experimentation is critical. There will be new pressures on institutions to establish clear, thoughtful guidelines for AI use that encourage its adoption in ways that result in effective, efficient, and engaging experiences for faculty and students alike. Now is the time to take risks and innovate. The good news is that many institutions are already leading the way." — Stephen Laster, CEO, Panopto

"AI is going to make it really obvious where learning was thin. When machines can do the routine stuff, schools are going to have to double down on what they can't automate. This could include things like reasoning, synthesis, discussion, and applied problem-solving. Those so-called ‘AI-proof' skills are going to matter more and more in both admissions and how students are evaluated. — Mike Magee, president, Minerva University

"Many universities' AI policies are disjointed. In 2026, higher education institutions must solidify and standardize their frameworks for students' AI usage. By creating clear guidelines at the institutional level, universities allow students the opportunity to learn how to use AI technologies as complements designed to enhance, not replace, their education. Additionally, setting an institutional standard for AI literacy promotes equitable use of AI technologies across student populations." — Michael Hale, chief learning officer, VitalSource

Return on Investment Will Become the Defining Standard for Ed Tech

"Technology spending in higher education is entering an age of accountability. After years of rapid adoption, institutions are beginning to focus less on the number of tools they use and more on the measurable impact those tools deliver. Leaders will increasingly demand evidence that technology improves decision-making, enhances efficiency, and contributes directly to student success. The question will shift from "What did we implement? to "What difference did it make? As institutions refine how they define and measure return on investment, technology will take on a new role, not as a collection of platforms, but as a connected system that drives insight, agility, and strategic advantage." — Sam Burgio, COO and president, Jenzabar

"Ed tech will face growing pressure to demonstrate clear, measurable ROI in learning outcomes, student success, efficiency, or revenue, especially as institutions confront demographic and funding challenges. LMS platforms in particular are evolving from content repositories into integrated hubs that connect communication, assessments, advising, registrar services, and even facilities and scheduling tools, often via standardized protocols. This shift will require significantly improved data sharing and interoperability, along with embedded analytics and reporting that serve faculty, advisors, student success teams, and institutional research while respecting growing privacy and security requirements. AI will increasingly be baked into these platforms — through personal learning agents, adaptive assessments, automated feedback, and 24/7 tutoring — making it even more critical that procurement processes scrutinize AI behavior, data usage, accessibility, and data ownership." — Nick Swayne, president, North Idaho College

Learners and Institutions Will Prioritize Workforce Readiness

"In 2026, the 'new traditional learner' is becoming the default. Tenures are shorter, careers are longer, linear paths are rarer, and people will engage with education throughout life. Instructure's recent "State of Learning and Readiness" research report found that 64% of U.S. workers plan to change jobs within the next two years. That kind of mobility will continue to raise expectations for flexible, work-relevant pathways and accelerate the global shift toward stackable credentials." — Ryan Lufkin, VP global academic strategy, Instructure

"2026 will finally be the year of digital credentials and digital competencies, as shifting employer needs and the rise of AI push institutions and industries toward a fully skills-driven ecosystem. AI-powered, personalized learning pathways will help learners identify the skills they need, develop those competencies, and earn verified digital credentials that demonstrate their readiness for a rapidly changing workforce. At the same time, sharing curriculum across different learning environments is becoming easier and more transparent. Learning materials are no longer confined to proprietary systems, which increases access, clarity, and long-term reliability. Improved interoperability will make it possible to clearly map and verify skills across frameworks, institutions, and organizations. This strengthens skills-first hiring practices and builds greater trust across the entire education and talent-development landscape." — Curtiss Barnes, CEO, 1EdTech

"Rising skepticism about higher education's value will collide with the rapid expansion of AI-enabled learning opportunities, setting the stage for a wave of disruption across the postsecondary sector in 2026. Students are already outpacing their instructors in using LLMs to accelerate learning, and soon Coursera's full catalog will be accessible through ChatGPT. Major employers are expanding their own education pipelines, while a growing cohort of AI-powered providers are increasingly personalized alternatives to traditional degrees. If workforce Pell grants are fully implemented, they could channel significant public funding toward these new pathways. Together, these forces will push students and employers to reimagine what counts as a valid credential and to look well beyond the two-year and four-year degree models." — Betheny Gross, director of research, WGU Labs

"In 2026, we'll see AI continue to shape how students evaluate their career paths. As automation transforms many entry-level white-collar roles, more students will gravitate toward hands-on, AI-resilient careers where human skill and expertise remain essential. We'll also see continued growth in student, parent, and school counselor interest in the skilled trades. Gen Z will stay focused on the return on investment of their education, seeking faster, more affordable pathways that lead directly to stable, in-demand jobs. Counselors, who play a key role in shaping postsecondary decisions, will increasingly highlight technical training options as strong, high-value alternatives to traditional four-year degrees. Once skeptical parents will follow suit, recognizing the strength of employer partnerships and the career potential these programs provide." — Tracy Lorenz, division president, Universal Technical Institute

Data Silos Will Be Broken as Integration Becomes Critical to Decision-making

"Right now, most institutions are trapped in a data bottleneck where closed-off systems and isolated applications force them into manual, spreadsheet-driven work and prevent them from seeing a complete, connected view of each student. Within the next year, more institutions will leverage open interoperability standards to unlock core systems and establish seamless, modern integrations throughout their learning ecosystem. As data flows become standardized, institutions will be able to link information across applications with far less noise and far more reliability. This shift could dramatically reduce the cost and expertise required to make sense of institutional data, replacing bespoke integrations with a unified approach that scales, and giving institutions the ability to easily and responsibility share the metrics needed for meaningful analytics and decision-making. Part of the success will require data sharing agreements that act as enablers instead of barriers by prioritizing best practices, shared responsibility, and inclusive stakeholder input. In this future, connected data becomes the norm, empowering educators with clearer insights and a holistic view of each learner." — Curtiss Barnes, CEO, 1EdTech

"The rapid rise of generative AI has made one thing unmistakably clear: Higher education can't unlock the value of AI until its data systems can actually talk to each other. Colleges have no shortage of data. What they need now is the interoperability required to make that data useful. Without that foundation, even the most advanced AI tools are little more than expensive investments with limited payoff. Most institutions I've seen operate in a maze of systems — enrollment, advising, degree audit, transfer evaluation, and registration — each essential, but rarely communicating. This lack of interoperability forces students and advisors to navigate across multiple platforms just to answer basic questions, creating artificial bottlenecks that slow decision-making, obscure academic progress, and make core processes far more complicated than they should be. Students lose momentum not because they lack ability or ambition, but because the infrastructure around them is misaligned. In 2026, I expect ed tech innovation to shift decisively toward fixing this integration problem. True interoperability, connecting existing workflows across platforms, will finally give students, faculty, and advisors a unified, real-time view of the academic journey. When systems work together, institutions can redeploy time and resources toward what matters most: supporting students, not managing complexity." — Sabih Bin Wasi, founder and CEO, Stellic

"As we approach 2026, U.S. universities will face increasing pressure to demonstrate tangible research impact amid heightened accountability and competition for funding. Traditional metrics capture academic reach but often overlook the other meaningful measure of success: how research drives real-world societal change, for example shaping policy. While many institutions track policy citations, few have the infrastructure to systematically translate insights into actionable outcomes, such as funding, donor engagement, or enhanced reputation. So my prediction is that more U.S. universities will develop comprehensive 'impact ecosystems,' integrating data infrastructure and governance to connect research to measurable societal outcomes. Policy citation data will move beyond a passive metric, becoming a strategic tool for impact storytelling. By translating research influence into concrete narratives, institutions will not only demonstrate value but also strengthen their competitive position, attract resources, and reinforce their societal contributions. 2026 will mark a turning point: Universities that successfully operationalize these systems will set new standards for policy engagement, transparency, and research-driven impact." — Euan Adie, CEO and founder, Overton

"The next generation of student success models will anticipate, not react. Institutions will harness connected data ecosystems that detect early signs of disengagement and trigger timely, personalized interventions, long before a student considers leaving. Success teams will operate as one, sharing real-time insights across advising, academic, and financial systems. Static reports will give way to live dashboards that predict outcomes and guide action. Colleges that embrace this shift will see higher retention, stronger completion rates, and a measurable rise in student confidence." — Sam Burgio, COO and president, Jenzabar