Generative AI in Higher Education: Uncertain Students, Ambiguous Use Cases, and Mercenary Perspectives
Abstract
Background: Students in higher education are using generative artificial intelligence (AI) despite mixed messages and contradictory policies. Objective: This study helps answer outstanding questions about many aspects of AI in higher education: familiarity, usage, perceptions of peers, ethical/social views, and AI grading. Method: I surveyed 733 undergraduates. Results: Students reported mixed levels of experience with AI and tended toward nervousness over excitement. Most reported professors addressing AI but not integrating it. While 41% of students had used AI in ways explicitly banned, many more students (59%) reported ambiguous use cases. Students overestimated peer cheating, and this predicted their own cheating, as did general experience with and excitement about AI. Meanwhile, 11% of students reported false accusations, with first-generation students possibly at a higher rate. Pragmatic views about career and inequality may be affecting behaviors. Men consistently reported more involvement with AI than women. Conclusion: Future research should focus on the hybrid collaboration of humans and AI and how AI might be leveraged to support and scaffold genuine learning. Teaching Implications: AI will be relevant to many future careers, and students increasingly want it to be part of their education. Academic integrity will be a continuing challenge, and students need transparency.
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A survey of 733 undergraduates revealed mixed familiarity with GenAI, with students more often reporting feelings of nervousness rather than excitement, due to unclear institutional policies and expectations.
“For instance, a survey of 733 undergraduates revealed mixed familiarity with GenAI, with students more often reporting feelings of nervousness rather than excitement, due to unclear institutional policies and expectations (Stone, 2024).”
From AI Literacy in the Communication Basic Course: Insights on Embedding, Motivation, and Reducing Uncertainty (2026) · cites this paper as Stone, 2024
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Hendal, B. A., & Chowdhury, G. G. (2026) · AI and Ethics · doi:10.1007/s43681-026-01431-6 · Cited by 1 · see citing works
How does conversation unfold? analyzing learner–LLM dialogues through multiple analytical lenses
Hidalgo, L., Pérez‐Sanagustín, M., do Nascimento, A. C. A., & Muñoz-Gama, J. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2739434
Generative AI Tools in Libyan Higher Education
Ghawail, E. A. A. (2026) · مجلة البحوث الأكاديمية · doi:10.65540/1daxv686
A RoadMap for Integrating AI into Applied Business Teaching: Cases from a Finance Course
D’Souza, J. (2026) · The National Teaching & Learning Forum · doi:10.1002/ntlf.70050
Lieske, S. N., & Wadley, D. (2026) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2026.2751526
- Authors
- Stone, B. W.
- Affiliations
- Boise State University (US)
- Corresponding author
- Stone, Brian W.
- ORCID
- Stone, Brian W.
- Venue
- Teaching of Psychology
- Publisher
- SAGE Publications
- Published
- 2024-12-20
- Volume
- 52
- Issue
- 3
- Pages
- 347-356
- ISSN
- 0098-6283 (ISSN-L)
- 1532-8023
- OpenAlex
- W4405646121
- Cited by
- 54
- References
- 48
- Citations by year
- 2026: 36 · 2025: 18
- FWCI
- 20.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Teaching of Psychology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- generative AI, university students, academic integrity, cheating, AI use in education, student perceptions, peer perception, AI grading, first-generation college students, gender differences, human-AI collaboration, undergraduate students
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