Key points:
- As the digital AI divide widens, how will institutions keep up and prepare students?
- Slashing budgets, saving futures: Can AI rescue higher ed?
- The ethical dilemma of AI in classrooms: Who decides what we teach?
- For more news on the digital AI divide, visit eCN’s AI in Education hub
As higher education continues to define how AI impacts and is used in teaching and learning, a new report offers insight into how higher education’s AI use will shape the world.
The 2025 EDUCAUSE AI Landscape Study: Into the Digital AI Divide, authored by EDUCAUSE’s Jenay Robert and Mark McCormack, examines AI’s impact on various aspects of higher education, including on leadership, policy, and the workforce. The report also features insights from open-ended answers from research respondents.
On the whole, respondents indicated a generally positive view of AI’s influence on higher education in the coming years, including regarding AI’s potential to benefit learning analytics and improve accessibility for students, faculty, and staff with disabilities. Respondents did express some pessimism in a few key areas, notably relating to personal misuse of the technology and the ethical implications of AI.
What is increasingly evident, however, is that higher education is moving into two camps–one with institutions that lack critical infrastructure and investments to keep pace with AI innovations despite plans and desires to do so, and another comprising institutions with the resources necessary to expose students and teachers to AI tools.
The report cautions that institutions in the second camp–those with early advantages–may have a more permanent competitive advantage, although time is needed to determine if this prediction will ring true.
“For now, we can mark a divide that is present and wider than it was only a year ago, and we will continue to examine and seek to address this divide in the years ahead,” the authors note.
Key findings include:
Strategy and leadership
- A larger proportion of respondents to this year’s survey agreed that “we view AI as a strategic priority” compared with last year’s respondents, at 57 percent and 49 percent, respectively.
- “Training for faculty” (63 percent) and “training for staff” (56 percent) topped the list of the most commonly selected elements in institutions’ AI-related strategic planning efforts.
- A mere 2 percent of respondents said that their institution is accommodating new AI-related costs through new sources of funding, and a plurality of executive leaders (34 percent) said that their institution has tended to underestimate AI-related costs.
Policies and guidelines
- The proportion of respondents reporting that their institution has AI-related AUPs increased from 23 percent last year to 39 percent this year, and only 13 percent of respondents reported that institution-wide policies have not been impacted by the emergence of AI.
- Only 9 percent of respondents reported that their institution’s cybersecurity and privacy policies are adequate for addressing AI-related risks to the institution.
Use cases
- Teaching and learning is the functional area at the institution most focused on using AI, with particular focus on the areas of academic integrity (74 percent), coursework (65 percent), assessment practices (54 percent), and curriculum design (54 percent).
- Two-thirds (68 percent) of respondents reported that students use AI “somewhat more” or “a lot more” than faculty, while only 2 percent reported that faculty use AI more than students, despite institutions’ strategically emphasizing faculty training over student training.
Workforce
- A plurality of respondents reported that their institution is supporting needed AI skills by upskilling or reskilling existing faculty or staff (37 percent) rather than by hiring new staff (1 percent).
- Asked about the AI-related skills needed among their faculty and staff, respondents highlighted “AI literacy” for both staff and faculty, as well as “boosting productivity” for staff and “best practices for teaching” for faculty.
The Digital AI divide between institutions
- Respondents from smaller institutions are remarkably similar to respondents from larger institutions in their personal use of AI tools, their motivations for institutional use of AI, and their expectations and optimism about the future of AI.
- Respondents from small and larger institutions differ notably, however, in the resources, capabilities, and practices they’re able to marshal for AI adoption.
Based on the report data, the authors offered recommendations and resources to close the digital AI divide in higher education:
- Shore up training for students to match the resources and efforts your institution is dedicating to faculty and staff training.
- Gather examples of institution-wide policies and guidelines you can share with your institutional leadership to encourage holistic and unified AI-related planning and management.
- Inventory common AI tools in use at your institution, and explore licensing or homegrown opportunities for solutions to help more effectively and safely procure and manage common AI solutions.
- For smaller institutions, build connections with peer institutions focused on resource sharing, knowledge and standards development, and business and strategic planning guidance.
- For larger institutions, make a plan to document your challenges and successes in adopting and managing AI technologies, and share with your peers across the larger higher education landscape.
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