Development of the GAI Dependence Scale: a validity and reliability study.
- 1Fujian Polytechnic Normal University, Fuqing, China.
- 2Fujian Normal University, Fuzhou, China.
Abstract
Background
While individuals use generative artificial intelligence (GAI) to enhance their productivity, they may also develop psychological and behavioral over-reliance on these tools. Evaluating the extent of an individual's dependence on GAI is crucial for conducting related research.Participants and procedure
The research involved 627 participants. Exploratory and confirmatory factor analysis was conducted to determine the construct validity of the scale. The results supported a structure consisting of four sub-dimensions and 19 items. The sub-dimensions are Usage Intensity, Content Dependence, Withdrawal Symptoms, and Negative Consequences.Results
Confirmatory factor analysis indicated good model fit. Significant relationships were found with the GAI Dependence Scale, AI Dependence Scale (version of six items), and Behavioral Intention Scale during the analysis of the scale's criterion validity. Cronbach's α internal consistency, and the test-retest method were used to assess the reliability of the scale. Cronbach's α internal consistency coefficient for the total score was found to be .92 for Sample 1 and .90 for Sample 2. Additionally, the test-retest reliability over a four-week interval yielded a coefficient of .81. Both coefficients are considered to indicate acceptable levels of reliability.Conclusions
The scale is an effective and reliable tool for assessing individual dependence on GAI.
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BibTeX
@article{zhang2026development,
title = {{Development of the GAI Dependence Scale: a validity and reliability study.}},
author = {Weizhong Zhang and Guangxian Leng and Yanfeng Xu},
journal = {Current issues in personality psychology},
year = {2026},
month = may,
doi = {10.5114/cipp/217837},
url = {https://europepmc.org/article/MED/42959587},
note = {CC BY-NC-SA},
}
RIS
TY - JOUR
TI - Development of the GAI Dependence Scale: a validity and reliability study.
AU - Weizhong Zhang
AU - Guangxian Leng
AU - Yanfeng Xu
PY - 2026
DA - 2026/05/15
JO - Current issues in personality psychology
DO - 10.5114/cipp/217837
UR - https://europepmc.org/article/MED/42959587
AB - Background While individuals use generative artificial intelligence (GAI) to enhance their productivity, they may also develop psychological and behavioral over-reliance on these tools. Evaluating the extent of an individual's dependence on GAI is crucial for conducting related research.Participants and procedure The research involved 627 participants. Exploratory and confirmatory factor analysis was conducted to determine the construct validity of the scale. The results supported a structure consisting of four sub-dimensions and 19 items. The sub-dimensions are Usage Intensity, Content Dependence, Withdrawal Symptoms, and Negative Consequences.Results Confirmatory factor analysis indicated good model fit. Significant relationships were found with the GAI Dependence Scale, AI Dependence Scale (version of six items), and Behavioral Intention Scale during the analysis of the scale's criterion validity. Cronbach's α internal consistency, and the test-retest method were used to assess the reliability of the scale. Cronbach's α internal consistency coefficient for the total score was found to be .92 for Sample 1 and .90 for Sample 2. Additionally, the test-retest reliability over a four-week interval yielded a coefficient of .81. Both coefficients are considered to indicate acceptable levels of reliability.Conclusions The scale is an effective and reliable tool for assessing individual dependence on GAI.
SN - 2353-4192
SN - 2353-561X
ER -