Ethical and Social Risk Awareness in Generative AI (GenAI): The Role of Mindset and GenAI Literacy

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Bibliographic Details
Title: Ethical and Social Risk Awareness in Generative AI (GenAI): The Role of Mindset and GenAI Literacy
Language: English
Authors: Sahin Gokcearslan (ORCID 0000-0003-3532-4251), Hatice Yildiz Durak (ORCID 0000-0002-5689-1805), Mustafa Serkan Gunbatar (ORCID 0000-0003-0485-3038), Nilufer Atman Uslu (ORCID 0000-0003-2322-4210), Aysun Nuket Elci (ORCID 0000-0002-0200-668X)
Source: International Journal of Technology in Education. 2025 8(4):914-937.
Availability: International Society for Technology, Education, and Science. ISTES Organization, Monument, CO 80132. e-mail: istesorganization@gmail.com; e-mail: ijteoffice@gmail.com; Web site: https://www.ijte.net/index.php/ijte/about
Peer Reviewed: Y
Page Count: 25
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Tests/Questionnaires
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, College Students, Knowledge Level, Student Attitudes, Beliefs, Test Construction, Test Validity, Foreign Countries, Social Problems, Risk, Ethics
Geographic Terms: Turkey
ISSN: 2689-2758
Abstract: GenAI's advanced natural language processing capabilities will revolutionize numerous areas, ranging from a paradigm shift in education to the economy. Along with the positive aspects of GenAI, ethical and social risks are also one of the negative aspects that attract attention in the literature. The purpose of this study is to test the role of mindset and GenAI literacy in university students' awareness of ethical and social risk with structural equation modeling. According to the research results, the developed awareness of ethical and social risk of generative artificial intelligence (GenAI) and the adapted GenAI literacy scale are valid and reliable. GenAI literacy variable does not have a significant effect on ethical and social risk awareness towards GenAI. Mindset should be taken into account in activities for both variables.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1486497
Database: ERIC
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