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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 0000-0003-1275-0282), Fengyu Cong
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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  Data: University Students' Privacy Concerns towards Generative Artificial Intelligence
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  Data: <searchLink fieldCode="AR" term="%22Ning+Wang%22">Ning Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Ying+Li%22">Ying Li</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-1275-0282">0000-0003-1275-0282</externalLink>)<br /><searchLink fieldCode="AR" term="%22Fengyu+Cong%22">Fengyu Cong</searchLink>
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  Data: 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
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  Data: 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.
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