What the Text is About
EDUCAUSE, a nonprofit that aims to advance higher education through the use of information technology, conducted an AI Landscape Study, which summarizes current sentiments and experiences related to strategic planning, leadership, policies, and other categories. We share some key trends and recommendations to help institutions navigate the evolving AI landscape and make informed decisions for the future.
In The AI Landscape in Higher Education, recent EDUCAUSE research explores the evolving role of AI in academia, drawing on responses from 910 professionals across higher education institutions. This study examines critical areas, including:
- How widespread is the institution’s approach to AI-related strategy?
- What are the primary motivators, if any, for AI-related strategic planning at an institution?
- What are the primary goals, if any, for AI-related strategic planning at an institution?
Based on participants’ insights, we’ll outline the key trends and challenges that are shaping AI’s integration into both academic and administrative realms within higher education.
01 Higher ed seeks consensus on AI use for learning and work
Higher education is increasingly focused on finding consensus on the appropriate use of AI for both learning and operational efficiency, underscoring the importance of a balanced, ethical approach. Faculty and administrators alike see that when used thoughtfully, AI can greatly enhance personalized learning, streamline administrative tasks, and offer new tools for both students and educators. Appropriate uses, such as providing tailored tutoring support, simplifying administrative processes, and assisting with research analysis, empower students and faculty to focus on higher-level tasks and more meaningful engagement. Faculty are right to be cautious about over-reliance on AI; however, in roles like digital literacy education, AI can help prepare students for an increasingly digital society and workforce. In classrooms, AI’s role as a teaching assistant—providing support in course design or improving material accessibility—can free educators to concentrate on what they do best: teaching and inspiring.
| Appropriate uses of AI | Inappropriate uses of AI |
|---|---|
| ✅ Personalized student support: tutoring, translating, academic/career advising, easing administrative processes, brainstorming, editing, accessibility tools and assistive technology ✅ Teaching assistant: course design, grading, providing feedback to students, providing feedback to faculty, improving accessibility of course materials ✅ Research assistant: finding and summarizing literature, sorting and analyzing data, predictive modeling, creating data visualizations ✅ Administrative assistant: automating tasks, drafting/revising communications (e.g., email), transcribing audio ✅ Learning analytics: analyzing and visualizing student success data, providing insights for student recruitment and retention ✅ Digital literacy education: preparing students to be part of the digital workforce and society | ⛔️ Trusting generative AI outputs without human oversight ⛔️ Representing AI-generated work as one’s own (e.g., using AI to write papers, take exams) Not citing AI as a resource for generated content ⛔️ Making high-stakes decisions (e.g., student admissions) without human oversight ⛔️ Conducting invasive data collection or surveillance ⛔️ Giving tools unauthorized access to sensitive data or intellectual property ⛔️ Relying on AI tools in place of human thought and creativity |
Faculty voices are crucial to shaping AI use, ensuring that ethical boundaries are respected, and showing students the value of human judgment alongside cutting-edge technology.
02 Institutional leaders are cautiously optimistic about AI
Institutional leaders’ approach to AI is marked by “cautious optimism,” as over half (52%) of executive leaders report a mix of enthusiasm and caution in adopting AI. Nearly a third (29%) of executives even express outright enthusiasm, seeing AI as a strategic advantage. However, among managers, directors, and frontline staff, sentiments are notably more reserved, with many adopting a cautious or even indifferent stance. This range of views highlights a need for cross-silo communication about AI’s role, as well as a strategic plan that reflects broad institutional buy-in. Although 73% of respondents report that AI-related strategies are in development in some or most units, central guidance and a clear leader in AI strategy are still lacking. Collaborative efforts are emerging, with grassroots and top-down strategies alike gaining traction, and over half (55%) of respondents note that cross-functional teams are already involved in AI strategy.
Next steps:
Faculty should actively engage in collaborative discussions about AI to ensure that institutional strategies align with teaching and learning needs. By sharing their unique perspectives, they can help shape AI initiatives that support educational goals and advocate for necessary resources and ethical guidelines. Joining interdisciplinary groups can also enable faculty to work alongside leaders and technical teams for a balanced, inclusive approach to AI adoption.
03 AI-related strategic planning goals and strategies are focused on supporting students’ experiences
AI-related strategic planning in higher education is increasingly centered on enhancing student experiences. While many institutions are driven by the need to remain competitive, the top strategic goals reflect a commitment to students: preparing them for the workforce (64%) and innovating in teaching and learning (63%). Beyond these core goals, respondents also highlight objectives like improving operational efficiency and promoting equity, inclusion, and accessibility. A significant majority (76%) of institutions report that their AI strategies aim to enrich educational experiences and student services, with secondary focuses on boosting administrative productivity and creating new educational models. Training emerges as the most common element in these strategies, especially for faculty, staff, and students, but there remains an opportunity to expand AI literacy specifically for students. The limited prioritization of long-term AI infrastructure, such as senior leadership roles and future budgeting, suggests that while institutions are actively adopting AI to enhance student outcomes, many have yet to fully establish sustainable frameworks for continued growth.
Inspiring resources for faculty:
- Join Generative AI Discussion Group by Center for Teaching and Learning at UMass Amherst.
- Complete Generative AI for Educators course by Google.
- Check out Resources for Educators from Microsoft Copilot.
- Strategies and examples of AI implementation in the classroom.
- A library of prompts that can turn popular AI models (GPT-4, Claude, Gemini, Bing) into a tutor, mentor, dialogue simulator, and much more.
04 Academic integrity is the most impacted element of teaching and learning
Academic integrity has emerged as the most significantly impacted aspect of teaching and learning in the context of AI, with 72% of respondents noting its effect. As AI tools make it easier for students to generate or alter content, educators are wrestling with new challenges in maintaining originality and honesty in academic work. Coursework and assessment methods are also being affected, as 62% and 47% of respondents, respectively, report impacts in these areas. Beyond integrity concerns, data security is a growing issue, as institutions find themselves underprepared to handle the privacy implications of AI use: only 18% of respondents feel their institutions have adequate technology to protect sensitive data. Among data security professionals, concerns are even more pronounced, with a strong focus on compliance with federal and local regulations, ethical governance, and mitigating biases in data. These combined factors underscore a pressing need for comprehensive strategies that address both integrity and security to responsibly integrate AI in education.
Inspiring resources for faculty:
- Assess opportunities and challenges with AI. The University of Michigan (UM) established a Generative Artificial Intelligence Advisory Committee to evaluate the opportunities and challenges of Generative AI, and how it impacts UM and its mission. Read their report.
- Five Tips for Writing Academic Integrity Statements in the Age of AI.