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

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