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Using Machine-Learning and Network Analysis to Investigate the Risk Factors of AI Dependence: The Crucial Role of Escape and Social Motivation

Truvace record
TRV-2026-2598raw data (JSON)
Version
v1Published
First published
Evidence type
Observational study
Sources
1 sourcePeer-reviewed
sha256
2f7052882ed2…check it

People have become accustomed to studying or working with the guidance of artificial intelligence (AI) in recent years. Studies have begun investigating the risk factors of AI dependence, though most have used hypothesis-testing methods. The present study aimed to investigate predictors of AI dependence using machine-learning and network analysis, which are data-driven approaches. The included risk factors were Big Five personality traits, self-efficacy, depression, social anxiety, adverse childhood experiences,…

The problem

In a study of 1,258 university students, using AI to escape or to meet social needs, along with social anxiety, were the strongest predictors of AI dependence.

Sources

1 on file
  1. Behavioral Sciencesdoi:10.3390/bs16050772
    Peer-reviewed2026-05-14

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v1

Truvace Impact Record TRV-2026-2598, v1: “Using Machine-Learning and Network Analysis to Investigate the Risk Factors of AI Dependence: The Crucial Role of Escape and Social Motivation.” Truvace, 2026-11-12. /record/TRV-2026-2598 (accessed on the day you copy it). sha256 2f7052882ed2ef32…

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