AI and Academic Integrity: Exploring Student Perceptions and Implications for Higher Education
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| Title: | AI and Academic Integrity: Exploring Student Perceptions and Implications for Higher Education |
|---|---|
| Language: | English |
| Authors: | Brady D. Lund (ORCID |
| Source: | Journal of Academic Ethics. 2025 23(3):1545-1565. |
| Availability: | BioMed Central, Ltd. Available from: Springer Nature. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: customerservice@springernature.com; Web site: https://www.springer.com/gp/biomedical-sciences |
| Peer Reviewed: | Y |
| Page Count: | 21 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, Man Machine Systems, Natural Language Processing, Student Attitudes, Technology Uses in Education, College Students, School Policy, Integrity, Cheating, Ethics, Student Characteristics |
| DOI: | 10.1007/s10805-025-09613-3 |
| ISSN: | 1570-1727 1572-8544 |
| Abstract: | The emergence of generative artificial intelligence tools, such as ChatGPT, presents new challenges impacting student perceptions of academic integrity. While extensive research exists on academic misconduct and student perceptions of various infractions, there is limited understanding of how AI tools impact these views and whether their use constitutes a violation of academic integrity policies. This study explores university students' awareness and perceptions of academic misconduct, particularly concerning AI tool usage. A survey of domestic and international students enrolled at major universities in the United States received 277 valid responses. The results reveal high awareness of university integrity policies and significant concern about the use of AI for writing papers, with substantial variance in perceptions of misconduct severity. Notably, using AI to write entire papers is seen as major misconduct by a majority, while smaller AI-assisted tasks are viewed as less severe. Regression analysis highlights the importance of ethical education, revealing that students who view AI writing as cheating and those who believe cheating is unethical perceive academic misconduct more seriously. Conversely, student demographics (major, educational level, gender, international status), awareness of AI detection tools, and perceived ethics of AI use show complex, often non-significant relationships with perceptions of misconduct severity. These findings provide indication that education and clear policy about AI usage and academic misconduct could be useful in addressing a growing number of infractions in the face of emerging AI trends. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1485736 |
| Database: | ERIC |
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