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Metacognitions about Generative AI Use: Psychometric and Network Analysis among Chinese College Students

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Title: Metacognitions about Generative AI Use: Psychometric and Network Analysis among Chinese College Students
Language: English
Authors: Yuntian Xie (ORCID 0000-0003-2869-4326), Ying Li, Taowen Yu, Yuxuan Liu
Source: Education and Information Technologies. 2025 30(14):20523-20542.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 20
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Information Analyses
Education Level: Higher Education
Postsecondary Education
Descriptors: Foreign Countries, College Students, Metacognition, Student Attitudes, Artificial Intelligence, Positive Attitudes, Negative Attitudes, Anxiety, Addictive Behavior, Predictor Variables
Geographic Terms: China
DOI: 10.1007/s10639-025-13584-8
ISSN: 1360-2357
1573-7608
Abstract: This study aimed to develop and validate the Metacognitions about Generative AI Use Scale (MGAUS) to assess college students' metacognitive beliefs about generative AI and to explore these metacognitions as predictors of generative AI addiction risk. A total of 1229 college students from China participated in the study, providing data through an online questionnaire. Exploratory factor analysis initially determined the MGAUS's structure, revealing two primary factors: "Positive metacognitions about generative AI use" and "Negative metacognitions about generative AI use", comprising nine items in total. Confirmatory factor analysis further validated the scale's stability and fit, as well as tested measurement invariance across gender, age, and educational levels. Correlation analysis indicated significant positive correlations between both positive and negative metacognitions and generative AI addiction. Additionally, negative metacognitions were significantly positively correlated with anxiety, whereas the correlation between positive metacognitions and anxiety was not significant. Multivariate regression analysis revealed that, after controlling for gender, both positive and negative metacognitions remained significant predictors of generative AI addiction, with negative metacognitions demonstrating stronger predictive power. A network analysis of the scale items further illuminated the close relationship between positive and negative metacognitions. Taken together, these findings contribute to the theoretical understanding of metacognition in the context of generative AI use and provide a scientific foundation for the prevention and intervention of generative AI addiction.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1484046
Database: ERIC
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: 10.1007/s10639-025-13584-8
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  Data: This study aimed to develop and validate the Metacognitions about Generative AI Use Scale (MGAUS) to assess college students' metacognitive beliefs about generative AI and to explore these metacognitions as predictors of generative AI addiction risk. A total of 1229 college students from China participated in the study, providing data through an online questionnaire. Exploratory factor analysis initially determined the MGAUS's structure, revealing two primary factors: "Positive metacognitions about generative AI use" and "Negative metacognitions about generative AI use", comprising nine items in total. Confirmatory factor analysis further validated the scale's stability and fit, as well as tested measurement invariance across gender, age, and educational levels. Correlation analysis indicated significant positive correlations between both positive and negative metacognitions and generative AI addiction. Additionally, negative metacognitions were significantly positively correlated with anxiety, whereas the correlation between positive metacognitions and anxiety was not significant. Multivariate regression analysis revealed that, after controlling for gender, both positive and negative metacognitions remained significant predictors of generative AI addiction, with negative metacognitions demonstrating stronger predictive power. A network analysis of the scale items further illuminated the close relationship between positive and negative metacognitions. Taken together, these findings contribute to the theoretical understanding of metacognition in the context of generative AI use and provide a scientific foundation for the prevention and intervention of generative AI addiction.
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      – SubjectFull: China
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