The higher education community continues to grapple with questions related to using artificial intelligence (AI) in learning and work. In support of these efforts, we present the 2025 EDUCAUSE AI Landscape Study, summarizing our community's sentiments and experiences related to strategy and leadership, policies and guidelines, use cases, the higher education workforce, and the institutional digital divide. The survey for this research project was distributed from November 4 to November 18, 2024, building on insights previously published in the 2024 EDUCAUSE AI Landscape Study.

For this research, we use the definition of AI established by the U.S. National Artificial Intelligence Act of 2020: "[A] machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments." With this as the basis, we asked survey respondents to consider AI broadly, though we recognize that it has become common for individuals to conflate general AI with generative AI.

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% and 49%, respectively.
• "Training for faculty" (63%) and "training for staff" (56%) topped the list of the most commonly selected elements in institutions' AI-related strategic planning efforts.
• A mere 2% of respondents said that their institution is accommodating new AI-related costs through new sources of funding, and a plurality of executive leaders (34%) 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% last year to 39% this year, and only 13% of respondents reported that institution-wide policies have not been impacted by the emergence of AI.
• Only 9% 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%), coursework (65%), assessment practices (54%), and curriculum design (54%).
• Two-thirds (68%) of respondents reported that students use AI "somewhat more" or "a lot more" than faculty, while only 2% 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%) rather than by hiring new staff (1%).
• 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.