探讨大学生人工智能（AI）使用动机的异质性、影响因素及其与心理健康的关系。研究1于2024年9月对5760名大学生进行问卷调查。潜在剖面分析结果发现,大学生AI使用动机可划分为工具动机主导型（28.09%）、高工具-娱乐动机型（30.57%）、中等多元动机型（36.02%）、全面高动机型（5.31%）四种亚类型。不同亚类型在心理健康指标上的表现也显著不同。研究2于2024年9月与2025年3月对221名大学生进行为期半年的追踪研究,探究AI使用动机与心理健康之间的双向关系。结果仅发现心理健康对AI使用动机的短期效应,表现为焦虑情绪增加AI使用中的逃避动机和社交动机,抑郁情绪增加逃避动机,而拥有意义感可预测工具动机。研究结果揭示了大学生AI使用动机的群体异质性及其与心理健康的关系模式,为个性化干预和心理健康促进提供实证依据。
Abstract
With the rapid advancement of artificial intelligence (AI) technology, an increasing number of university students are integrating AI into their academic studies and daily life. Based on literature review, the present empirical research examining the heterogeneity of motivations for AI use and its causal relationship with mental health is relatively limited, resulting in an inadequate understanding of the “empowering” potentials and “risk” concerns associated with AI use among university students. Moreover, existing research on the relationship between AI use motivations and psychological health is scarce, predominantly cross-sectional, and largely focused on negative indicators, restricting a comprehensive understanding of their mutual relationship. According to the dual-factor model of mental health, it is crucial to consider both positive and negative psychological health indicators when evaluating the association of AI use motivations and mental health, providing a more comprehensive and objective reflection of individual psychological well-being. Therefore, this study examines the relationship between four types of AI use motivations and mental health among university students by selecting flourishing and meaning in life as positive psychological indicators, and anxiety and depression as negative psychological indicators, to better understand the “empowering” and “risk” roles of AI use among university students.
This study used both cross-sectional and longitudinal designs to investigate the heterogeneity and influencing factors of university students’ motivations for AI use and their relationships with mental health. Study 1 was a cross-sectional survey of 5,760 university students from cities including Beijing, Shanghai, Zhengzhou, and other cities in China. All participants completed a questionnaire regarding university students’ AI use and mental health. Latent profile analysis identified four subtypes of AI use motivations: the tool-oriented type (28.09%), the high tool-entertainment motivation type (30.57%), the moderate multi-motivation type (36.02%), and the globally high motivation type (5.31%). Gender, grades, place of birth, and major effectively predicted these subtypes of AI use motivations among university students. Moreover, significant differences in mental health levels were observed among the identified latent classes of AI use motivations, with the efficiency high tool-entertainment motivation group scoring the highest, followed sequentially by the tool-oriented, moderate multi-motivation, and globally high motivation groups, which scored the lowest.
In Study 2, 221 university students from a university in Henan Province were selected to participate in a six-month longitudinal survey exploring the bidirectional relationship between AI use motivations and mental health. Cross-lagged analysis indicated that anxiety significantly predicted escapism motivation and social motivation, depression significantly predicted escapism motivation, and a higher sense of meaning significantly predicted instrumental motivation, while AI use motivation did not significantly predict mental health indicators.
The findings reveal the heterogeneity of university students’ motivations for using AI and their relationship with mental health. These results provide empirical evidence for personalized interventions and psychological health promotion strategies. However, there are some limitations need further consideration. First, the longitudinal survey included only two waves of data collection over a six-month interval, with a relatively small and homogeneous sample. Future research could increase the number of survey waves (e.g., three or more), expand the sample size, and enhance sample heterogeneity to more comprehensively and accurately reveal the long-term relationship patterns between AI use motivations and mental health. Second, the survey in this study was conducted from 2024 to 2025, at which time the overall intensity of university students’ motivations for AI usage was not yet prominent. With the rapid popularization and deeper integration of generative AI technologies among university students, the intensity and structure of motivations for AI use within this group may change. Therefore, future research should continuously observe and dynamically track the evolving trends of university students’ AI use motivations, capturing their actual conditions and patterns of change more accurately in the current context.
关键词
人工智能使用动机 /
心理健康 /
大学生 /
潜在剖面分析 /
交叉滞后效应分析
Key words
artificial intelligence use motivation /
mental health /
university students /
latent profile analysis /
cross-lagged panel analysis
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基金
*本研究得到全国教育科学规划教育部青年项目（EEA240407）的资助
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