Validation of the generative artificial intelligence dependency scale among Chinese college students

OBJECTIVE: To update the Generative Artificial Intelligence (AI) Dependency Scale and assess its reliability and validity among college students in China. METHODS: The Chinese version of the Generative Artificial Intelligence (AI) Dependency Scale was administered to 986 university students. The Generative AI Addiction Scale and the Chinese Big Five Personality Questionnaire Short - Form were used as comparators. The sample was randomly divided into two groups: 491 participants (Sample 1) for item analysis and exploratory factor analysis, and 495 participants (Sample 2) for confirmatory factor analysis. A subsample of 324 participants from both groups underwent retesting after 4 weeks. RESULTS: /41 = 2.29, P < 0.001, RMSEA = 0.05, SRMR = 0.03, CFI = 0.97, TLI = 0.96); the total score of the Generative AI Dependency Scale showed significant positive correlations with generative AI addiction, openness, agreeableness, neuroticism, extraversion, and conscientiousness (r = 0.17-0.75, P < 0.01); the internal consistency coefficient of the scale was 0.932, with coefficients ranging from 0.804 to 0.948 for each dimension' s mean score; the test - retest reliability (ICC) for the total scale was 0.851, with ICCs for each dimension' s mean score ranging from 0.628 to 0.788 (P < 0.001); based on a three - factor model, cross - gender and cross - grade measurement invariance was assessed for form invariance, weak invariance, strong invariance, and strict invariance. ΔCFI and ΔTLI values were both <0.01, while ΔRMSEA values were all <0.015, indicating that the generative AI dependency scale possesses measurement invariance across genders and academic years. CONCLUSION: The Chinese version of the Generative Artificial Intelligence (AI) Dependency Scale is a useful assessment tool for determining generative AI dependency among Chinese university students because it shows strong validity, reliability, and measurement invariance.