National Survey: 95% of College Faculty Fear Student Overreliance on AI and Diminished Critical Thinking Among Learners Who Use Generative AI Tools
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A survey of college and university faculty nationwide finds widespread concern and skepticism about how generative artificial intelligence (GenAI) is affecting their teaching and student performance across academic disciplines. Large majorities warn that these tools will lead to student overreliance on AI, weaken their critical thinking, shorten their attention spans, and erode academic integrity and the value of college diplomas – concerns they say strike at the heart of higher education’s mission.
At the same time, many think that teaching AI literacy is important, that their students’ future jobs will be seriously impacted by the spread of GenAI and that it is vital for those in higher education to stress the ethical, environmental, and social consequences of AI use.
These new findings come from a November survey of 1,057 faculty by the American Association of Colleges and Universities and Elon University’s Imagining the Digital Future Center.
Key findings:
- 95% of the faculty in this survey said GenAI’s impact will be to increase students’ overreliance on these artificial intelligence tools, including 75% who said the tools will have a lot of impact.
- 90% said the use of GenAI will diminish students’ critical thinking skills, including 66% who think GenAI will have a lot of impact.
- 83% said the use of GenAI will decrease student attention spans, including 62% who thought GenAI will have a lot of impact.
- 86% said they believe it is likely or extremely likely that the emergence of GenAI tools will impact the work and role of those who teach in higher education.
- 79% think the typical teaching model in their department will be affected by GenAI tools at least to some extent, including 43% who said they believe the impact will be significant.
- 78% said cheating on their campus has increased since GenAI tools have become widely available, including 57% who said it has increased a lot. And 73% said they have personally dealt with academic integrity issues involving their students’ use of GenAI.
- 48% said their students’ research has gotten worse because of GenAI, compared with 20% who said they believe it has gotten better.
- 74% of these faculty said the use of GenAI tools will affect the integrity and value of academic degrees for the worse, including 36% who said the value of degrees will worsen a lot. Just 8% said GenAI’s impact will affect the value of degrees for the better.
- 63% said their schools’ graduates in spring 2025 were not very or not at all prepared to use GenAI in the world of work, compared with 37% who felt the graduates were very or somewhat prepared.
“These faculty are divided about the use of generative AI itself,” said Lee Rainie, director of Elon University’s Imagining the Digital Future Center and a co-author of the report. “Some are innovating and eager to do more; a notable share are strongly resistant; and many are grappling with how to proceed. At the same time, there is broad agreement that without clear values, shared norms and serious investment in AI literacy, we risk trading compelling teaching, deep learning, human judgment and students’ intellectual independence for convenience and a perilous, automated future.”
Eddie Watson, co-author of the report and Vice President for Digital Innovation at AAC&U, added: “When more than nine in ten faculty warn that generative AI may weaken critical thinking and increase student overreliance, it is clear that higher education is at an inflection point. These findings do not call for abandoning AI, but for intentional leadership – rethinking teaching models, assessment practices, and academic integrity so that human judgment, inquiry, and learning remain central. The challenge before higher education is to act with urgency and purpose so that AI strengthens, rather than undermines, the value of a college degree.”
A profession coming to terms with AI, but not feeling prepared
Despite these concerns, the report finds that faculty are not uniformly opposed to AI. Many acknowledge potential benefits, particularly in personalized instruction and efficiency, and a majority are already engaging students in discussions about AI’s limitations and risks.
- 69% of faculty say they address AI literacy topics—such as bias, hallucinations, misinformation, privacy and ethics—in their teaching.
- 61% believe GenAI could enhance or customize learning in the future.
- 87% report that they have created explicit policies for students on acceptable and unacceptable uses of AI in coursework.
At the same time, faculty describe a fragmented policy environment. Some 48% say their institution has clear, campus-wide guidelines for AI use in teaching and learning, and just 35% say their departments have done so.
Faculty also report that many institutions are unprepared for the scale of change AI is bringing:
- 59% say their institution is not well prepared to use GenAI effectively to prepare students for the future.
- 68% say their school has not adequately prepared faculty to use GenAI for teaching or mentoring.
- 67% said their schools have not prepared their non-faculty for using GenAI to perform their work.
When asked about longer-term consequences of AI’s impact on higher education, more often than not, faculty expressed worry:
- 49% say GenAI’s impact on students’ future careers will be more negative than positive, compared with 20% who see more positive than negative effects.
- 62% believe GenAI will worsen student learning outcomes over the next five years.
- 54% say GenAI will have a more negative than positive impact on students’ overall lives at their institution.
About the Study
This non-scientific survey was conducted between October 29 and November 26, 2025, using a list of college and university faculty members developed by AAC&U and Elon University. The sample of 1,057 respondents is diverse in a range of academic disciplines, school sizes, job titles and composition of student populations, but the data reported here are not generalizable for the entire population of college faculty members. Full methodology details and topline findings are available here.