What does this research mean for the field?

College students' mental health symptoms are more closely associated with the relational quality and emotional attachment of their interactions with Generative AI than with the mere frequency or intensity of its use. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.ESTABLISHES_NEW_DIRECTION.

What question did this study set out to answer?

To identify distinct patterns of generative AI interaction among college students and evaluate their associations with depressive and anxiety symptoms.

September 2, 2026Scientific ReportsOpen Access

Generative AI interaction styles and mental health among college students: a latent profile analysis

View Full Paper

Ask AI

Bookmark

Authors

ZLZongming LiuShandong University SYShuai YangShaanxi University of Science and Technology PFPing Fan

Discussion

Member takes

Overview

Cross-sectional study reveals that ambivalent generative AI attachment links to elevated depressive and anxiety symptoms in college students, indicating the importance of relational engagement...

Key Points

To identify distinct patterns of generative AI interaction among college students and evaluate their associations with depressive and anxiety symptoms.
Surveyed 7,029 college students across six universities in China.
Conducted latent profile analysis using five interaction indicators: use intensity, perceived quality, trust, psychological closeness, and emotional attachment.
Employed random forest and SHAP analyses to determine psychological and behavioral predictors of profile membership.
Identified five distinct user profiles: Exploratory-Casual (31.97%), Efficient-Instrumental (27.00%), Detached-Avoidant (19.62%), Deep-Emotional (12.18%), and Ambivalent-Attached (9.23%).
Observed the highest levels of depressive and anxiety symptoms in the Ambivalent-Attached profile, with mean depressive symptoms reaching the moderate clinical range.
Demonstrated that mental health outcomes align more strongly with relational quality indicators than with use intensity, with smartphone addiction and neuroticism serving as top predictors.

Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/6a97e275c562ede874ec69bd https://doi.org/10.1038/s41598-026-68063-y

View Full Paper

Ask AI

Bookmark

September 2, 2026Scientific ReportsOpen Access

Generative AI interaction styles and mental health among college students: a latent profile analysis

View Full Paper

Ask AI

Bookmark

Authors

ZLZongming LiuShandong University SYShuai YangShaanxi University of Science and Technology PFPing Fan

Discussion

Member takes

Overview

Cross-sectional study reveals that ambivalent generative AI attachment links to elevated depressive and anxiety symptoms in college students, indicating the importance of relational engagement...

Key Points

To identify distinct patterns of generative AI interaction among college students and evaluate their associations with depressive and anxiety symptoms.
Surveyed 7,029 college students across six universities in China.
Conducted latent profile analysis using five interaction indicators: use intensity, perceived quality, trust, psychological closeness, and emotional attachment.
Employed random forest and SHAP analyses to determine psychological and behavioral predictors of profile membership.
Identified five distinct user profiles: Exploratory-Casual (31.97%), Efficient-Instrumental (27.00%), Detached-Avoidant (19.62%), Deep-Emotional (12.18%), and Ambivalent-Attached (9.23%).
Observed the highest levels of depressive and anxiety symptoms in the Ambivalent-Attached profile, with mean depressive symptoms reaching the moderate clinical range.
Demonstrated that mental health outcomes align more strongly with relational quality indicators than with use intensity, with smartphone addiction and neuroticism serving as top predictors.

Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/6a97e275c562ede874ec69bd https://doi.org/10.1038/s41598-026-68063-y

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark