University Students' Privacy Concerns towards Generative Artificial Intelligence
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| Title: | University Students' Privacy Concerns towards Generative Artificial Intelligence |
|---|---|
| Language: | English |
| Authors: | Ning Wang, Ying Li (ORCID |
| Source: | Journal of Academic Ethics. 2025 23(4):2401-2422. |
| 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: | 22 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research Tests/Questionnaires |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | College Students, Student Attitudes, Privacy, Artificial Intelligence, Ethics, Technology Uses in Education, Bias, Peer Influence, Mental Health, Equal Education |
| DOI: | 10.1007/s10805-025-09658-4 |
| ISSN: | 1570-1727 1572-8544 |
| Abstract: | Privacy concerns are among the most critical ethical issues in applying generative artificial intelligence (GAI) tools in education. This study examines university students' privacy concerns regarding GAI, investigating the causes, consequences, and educational implications of these concerns. We employed a qualitative research design featuring in-depth, semi-structured interviews with 15 university students, guided by the Antecedents-Privacy Concerns-Outcomes (APCO) macro model as our theoretical framework, while utilizing a combination of thematic analysis and grounded theory to identify emergent patterns and relationships within participants' responses. Our research examined students' privacy concerns regarding GAI usage, investigating influencing factors at both individual and social levels. At the individual level, we analyzed privacy experiences, optimistic bias, and privacy protection skills, while at the social level, we explored peer influence and perceived effectiveness of privacy policies. We also assessed how these privacy concerns ultimately impacted three crucial outcomes: students' use of GAI tools, mental health, and educational inequality. This study addresses a significant research gap by examining privacy concerns among university students regarding GAI usage, a previously understudied area despite students being early adopters of emerging technologies, while simultaneously representing a vulnerable group regarding privacy risks. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1485309 |
| Database: | ERIC |
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