Artificial intelligence is no longer a future-facing experiment in education. For many schools and institutions, it is quickly becoming a practical response to shrinking budgets, staffing shortages, and mounting administrative workloads.
A new EdTech Trends 2026 report from Jotform underscores this shift, finding that 65 percent of educators are already using AI tools in their day-to-day work. The takeaway is not that educators are chasing the latest technology trend, but that they are seeking relief from systemic pressures that have intensified over the past several years.
AI adoption is being driven by workload, not novelty
The report makes clear that AI adoption is largely pragmatic. Nearly half of educators using AI say they apply it to both instructional and administrative tasks. From summarizing long documents and drafting communications to supporting lesson planning, AI is being used to reduce time spent on repetitive work rather than to replace teaching itself.
This distinction matters. Educators are not embracing AI to automate learning, but to protect time for it. As responsibilities expand beyond the classroom, AI is increasingly viewed as a productivity layer that helps educators stay focused on students instead of paperwork.
Tool sprawl is limiting the impact of edtech investments
Despite widespread technology adoption, the report highlights a persistent problem for education IT leaders: fragmentation. While 77 percent of educators say their digital tools work well individually, nearly three quarters cite poor integration between systems as a major frustration.
Educators report using an average of eight digital tools, and many describe the experience as overwhelming. Even with these tools in place, manual tasks still consume several hours each week. This disconnect illustrates a broader issue across education technology. The challenge is no longer access to tools, but how well those tools work together.
AI is filling gaps created by staffing and funding pressures
Concerns about infrastructure funding cuts were cited by more than half of survey respondents, reinforcing the reality that many institutions are being asked to do more with fewer resources. In this environment, AI is increasingly positioned as a stopgap solution.
Most educators report using AI for research, brainstorming, and content creation, tasks that often spill into evenings and weekends. By accelerating these workflows, AI helps mitigate burnout without fundamentally changing how education is delivered.
However, relying on AI to offset staffing and budget challenges also raises questions about sustainability. Without thoughtful implementation, AI risks becoming another layer of complexity rather than a meaningful solution.
Ethics and trust remain unresolved
The report also reflects ongoing concerns around data security and ethical use. As AI tools handle sensitive student and institutional information, educators are rightly cautious about transparency, privacy protections, and long-term accountability.
These concerns are not barriers to adoption, but they are signals that governance and policy must evolve alongside technology. Trust will play a decisive role in determining how deeply AI becomes embedded in education systems.
Integration will define the next phase of AI in education
What emerges most clearly from the report is that AI’s future impact will depend less on innovation and more on integration. Educators are already willing to use AI, but they want fewer platforms, cleaner workflows, and systems that communicate with one another.
For education IT leaders, the next phase of AI adoption will be about ecosystem design rather than tool selection. Institutions that prioritize interoperability, training, and ethical frameworks will be better positioned to turn AI from a tactical fix into a strategic asset.
In education, AI is no longer about what is possible. It is about what is practical, sustainable, and aligned with the realities educators face every day.
Artificial intelligence is no longer an emerging topic in higher education — it’s a core operational and pedagogical concern for campuses across the country. A new Inside Higher Ed analysis outlines five key ways AI will continue to reshape colleges and universities in 2026, offering a useful lens for IT leaders, academic technologists, and digital strategy teams.
The Future of AI Depends on the Tech Market and Public Perception
AI’s trajectory in higher education isn’t defined only by what happens within campuses — it hinges on broader societal attitudes and the health of the AI sector itself. If the AI market softens or faces high-profile setbacks, institutional momentum around new tools and investments could slow. Conversely, continued innovation and adoption could solidify AI as a central pillar of academic infrastructure.
This also ties directly to how potential students and employers perceive the value of traditional degrees versus AI-driven learning pathways — a conversation that will intensify throughout 2026.
Scaling AI Initiatives Means Measuring Impact
Institutions have moved past siloed pilots of AI tools. Next, they’re looking to scale strategic deployments — from campuswide AI literacy initiatives to enterprise-level services — while measuring outcomes. Leaders are increasingly asking:
How do we evaluate the ROI of AI tools?
What metrics matter most — learning gains, retention, operational efficiencies?
This shift from experimentation to strategic planning and evaluation marks a maturation of AI use in higher education.
A Period of AI Disillusionment May Be Ahead
Not all momentum will be linear. Some educators and students are already expressing fatigue or skepticism about AI’s place in the classroom. Ongoing use exposes limitations — whether ethical, pedagogical, or technological — and stimulates reflection about why we use these tools and what we hope they’ll achieve.
Rather than rejecting AI entirely, this phase could help campuses deepen critical engagement with both technology and core academic values.
Building Stronger Connections Between Tech and Campus Communities
AI adoption isn’t just about tools — it’s about people. Effective use in 2026 will depend on:
IT and ed-tech leaders becoming communicators and trainers
Faculty and staff empowered to integrate AI responsibly
Students equipped with the skills to use and critique AI
This involves strengthening governance, trust, and shared decision-making across institutional units — not just installing new software.
Reducing Fragmentation to Boost Efficiency
One of the most tangible promises of AI lies in connecting systems that historically don’t “talk” to one another: advising, enrollment, financial aid, billing, LMS data and more. AI-driven orchestration and automation could:
Eliminate redundant processes
Provide unified insights for administrators and advisors
Improve student experiences with smoother workflows
Rather than layering new platforms on top of old ones, institutions are looking to intelligently weave existing systems together.
What This Means for IT Leaders
For technology professionals in higher education, these predictions reinforce a few clear priorities for 2026:
AI governance and measurement are as important as tool selection.
Human-centered support and training will determine whether AI helps or hinders outcomes.
Operational integration of AI must be paired with transparency, ethics, and alignment to institutional missions.
In other words: AI isn’t just a technical challenge — it’s a strategic transformation. And campuses that treat it as such will be better positioned to deliver value for students, faculty, and the institution as a whole.
By Christian Young, Pro AV product manager, ATEN Technology.
The adaptation to the “new normal” in education pushed schools and other learning facilities to evolve and transform their classrooms into hybrid environments. This allowed faculty to instruct students on campus and online at the same time to meet their curriculum and complete their teaching calendar periods on time.
The main benefit of virtual classroom solutions is that by facilitating collaboration and synchronous learning – allowing active participation and interaction with the teacher in real-time – they create a learning environment that is most analogous to a physical classroom.
However, teachers were exposed to technology and methodologies that were not part of their daily routine or that they may not have experienced before, so their learning curve to comply with this adaptation had to be rapidly enforced. In addition, more online content creation spaces were needed, and physical lab spaces were compelled to be virtualized. At the same time, education providers needed to balance these hybrid learning setups with new forms of live or asynchronous learning and content delivery methods to avoid hybrid fatigue.
These opportunities for smart classrooms presented challenges that solution providers needed to overcome as well. For example, solutions must integrate and work seamlessly with diverse Professional AV (Pro AV) equipment, multimedia devices, and control systems in existing classrooms. These solutions must be easily implementable yet scalable and present protection against cyber threats as more classes move to an online platform. Also, the delivery of content should be dependable and accurate. These solutions should focus on the student experience, offering collaborative functions so interactivity can provide motivation and improve learning outcomes.
Schools have started to look at technology that may have been only seen just for corporate or even government applications only. As interactive multimedia classrooms become more popular, there is an increase in the kinds of devices being used in these hybrid environments. More content needs to be displayed, and this will see both more displays in total and an increase in the ways that displays are utilized. Livestreaming and broadcasting are now essential elements of the hybrid classroom, especially in PBL (project-based learning) scenarios. Video signal transmissions need to be bidirectional for fully interactive learning.
The first full school year of remote, in-person, or hybrid model of instruction during the COVID-19 era is finally behind us. Now that the dust is settling on that tumultuous time for students, teachers, parents, and administrators, we need to reflect and dissect the cases of perseverance and success and how they came about so that we can be ready to grapple with the challenges that we will surely meet going forward. Indeed, there are many lessons to be learned from the past year, but possibly the most critical is the need for school systems in every community to have a plan to deliver technology-enabled instruction to students.
Leveraging technology to improve student outcomes is not a new concept but it is a practice typically done in isolation and at the maximum comfort level of the teacher. Many instructors to this day reject the full capabilities of what advanced technologies can provide in favor of a twentieth-century model of manual computing and labor because that is “how it has always been done.” In reality, new and innovative technologies, coupled with the ability to digitize and deliver content remotely, has expanded the capabilities of teachers to personalize instruction based upon a student’s interest, learning style, aptitude, and countless other factors because no two kids are the same. In essence, the traditional paradigm has been flipped from “students go to school to learn” to “learning goes to students wherever they are.”
A May 2020 study examining the impact of school closures on student learning demonstrated that students in schools using technology-enabled instruction, in this case a reading software that helps teachers differentiate instruction for student in grades 2-12–Achieve3000 Literacy, continued to attain similar levels of reading growth during school closures as they had earlier in the year.
By Matt Guenin, chief commercial officer, ElectrifAi.
Matt Guenin
In the wake of the COVID-19 pandemic, higher education institutions are now facing increasing challenges to attract students and grow student enrollment. This challenge has been magnified by the financial crisis with a large number of students reluctant to begin or return to school this fall, especially as concerns with growing unemployment spike record numbers.
Colleges and universities face unprecedented challenges to ensure full classes of qualified and promising students, and traditional recruitment tactics are proving ineffective.
Institutions must find a better way to define their target student and optimize enrollment in a more competitive market. Fortunately, artificial intelligence (AI) and machine learning (ML) is offering higher education an innovative way to do this more effectively.
Momentum building to drop test-based admissions scores
Up until recently, schools have used SAT/ACT/GMAT/GRE scores as a key measure of qualification for admission. But even before the pandemic hit, there was much debate over the efficacy of how these scores could predict academic success or even career potential. Now, with online classes and virtual testing adding another layer of uncertainty to this process, major university systems like California have dropped this requirement given concerns about fairness.
Many more schools are considering dropping test scores entirely and several institutions, including the University of Chicago, the University of Rochester, and Marquette University, have already moved to a test-optional policy to help attract a broader range of college applicants.
The University of Rochester, which generally receives a high number of applicants, found that having a “test flexible” period made it evident that test scores added little value to the admissions decision process. Marquette chose to drop the requirements for test scores as part of a campaign to attract a more diverse student pool.
The impact of the COVID-19 on student enrollment
While numerous higher education institutions have worked for years to compete more effectively given declining enrollment trends, the COVID-19 pandemic has been a catalyst for change. Lower enrollment is forecasted this fall as a result of financial disruption, social distancing policies, and concerns with shift to online learning.
Many universities also must face loss of international students given latest restrictions. Essentially, pent up concerns that have been building for years regarding the admissions process for higher education are being exasperated by COVID-19, paired with travel and social distancing restrictions – we’ve created the perfect catalyst for a year of enrollment unlike any before.
Admissions departments will face increasing challenges and disruption given these structural and macro issues in higher education, resulting in a cascade effect across the system covering Tier 1, 2, 3, down through the community colleges.
We will have to learn new ways to make admissions decisions in the face of uncertainty and increased competition by other schools, of which will require largely different approaches for large state schools, regional universities, or small private colleges.
By Randy Lack, safety, security and computer vision manager for the Americas, Dell Technologies.
Randy Lack
Many colleges and universities are working to take advantage of Internet of Things (IoT) technologies to build “smart campuses” that promise new peace of mind for students and their families and a better overall experience for all who set foot on campus.
Schools are the largest market for video security systems in the U.S., with an estimated $450 million spent in 2018. Adoption will continue to increase as IoT-enabled security solutions come onto the scene—empowering colleges and universities to do more than monitor security cameras and investigate after-event footage.
New kinds of devices and powerful analytics, including artificial intelligence (AI) and machine learning, are transforming cameras and sensors from passive data collectors into intelligent observers with the ability to recognize and alert security to potential problems, provide real-time insight during unfolding events, and help identify patterns to proactively deter and prevent problems.
Smart IP cameras with “computer vision” can learn over time to recognize patterns and behaviors in order to zero in on suspicious activity and better predict the likelihood of events. These cameras, combined with sensors that can detect sound, temperature, vibration, chemicals and more, form a system that can alert security to potential problems by relying on insights delivered from analytics-driven interconnected IoT devices.
As a result, security teams can help improve response, share critical information with first responders, make better use of available resources, and help prevent situations from escalating or in some cases, help prevent them from occurring in the first place.
The following are just some of the innovative secure-campus applications being deployed today:
Real-time integrated dispatch solutions that enable live video streams and location mapping to be shared with community police, fire, and other first responders, for faster, more coordinated response
Sensor, floorplan and GPS data that combine with incident monitoring, push notifications, and the ability to pinpoint the location of anyone with a cell phone. This allows first responders to isolate events to send in the right kind of help to where it’s needed more quickly.
The open visibility of 24/7 IoT technologies such as security cameras across campus serve as a deterrent, helping to prevent theft, assault and vandalism
Compact, solar-powered, Wi-Fi / 4G / RF-connected devices help cover “blind spots” without the expense of permitting an infrastructure investment to bring power to them
Smart lighting follow people across dark campuses
“Escort drones” accompany students and staff from one location to another
The need for a holistic, integrated approach
To take advantage of these applications, it’s important to understand that security is no longer confined to self-contained, standalone systems and departments. With IoT, campus safety becomes a widely distributed, networked, and data-driven solution, with new requirements for shared campus policies and IT modernization across infrastructure, security, data management, analytics, operations, software development, and more.
Indeed, many HiEd safety solutions require integration with security and IT organizations beyond the physical campus. For example, a large urban campus in southern California and surrounding city government are working together to tie together data from campus, municipal and even the shuttle buses that transport students to and from the city for cultural and sporting events. The solution being developed also enables city and campus police to log in to each other’s systems when coordinated efforts are needed.
Artificial intelligence is no longer a future-facing experiment in education. For many schools and institutions, it is quickly becoming a practical response to shrinking budgets, staffing shortages, and mounting administrative workloads.
Artificial intelligence is no longer an emerging topic in higher education — it’s a core operational and pedagogical concern for campuses across the country. A new Inside Higher Ed analysis outlines five key ways AI will continue to reshape colleges and universities in 2026, offering a useful lens for IT leaders, academic technologists, and digital strategy teams.
