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TRUVACE RECORD VERSION
record: TRV-2026-2598
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T08:00:18.451482Z
status: published
lens: p_space
sector: health
headline: Using Machine-Learning and Network Analysis to Investigate the Risk Factors of AI Dependence: The Crucial Role of Escape and Social Motivation
dek: 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,…
gain_title: (none)
problem_title: 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.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: 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.
problem_evidence: escape and social motivation for AI use, along with social anxiety, were the main predictors of AI dependence
quick_read: A study of 1,258 university students (942 females and 316 males, mean age 22.11) found that why students use AI matters more than how much. Escape motivation, social motivation and social anxiety were the main predictors of AI dependence.

Network analysis pointed the same way: escape and social motivation were the most central nodes, with the highest Expected Influence indices. The authors say this suggests that, when addressing mental health problems tied to AI dependence, focusing on emotional isolation and social interaction difficulties is more effective than simply cutting down on AI use.

The work used data-driven methods rather than the hypothesis testing that most earlier studies of AI dependence relied on.
limitation: 
tag: Evidence-backed problem
key_points: The sample was 1,258 university students: 942 females and 316 males, with a mean age of 22.11 years (SD = 2.69). | Four machine-learning algorithms were tested: Elastic Net, Random Forest, XGBoost and LightGBM. | Risk factors examined included Big Five personality traits, self-efficacy, depression, social anxiety, adverse childhood experiences and AI use motivation. | Escape and social motivation for AI use, plus social anxiety, were the main predictors of AI dependence. | In the network analysis, escape and social motivation had the highest Expected Influence indices.
rundown: Researchers chose the risk factors based on theory and earlier empirical studies. They covered Big Five personality traits, self-efficacy, depression, social anxiety, adverse childhood experiences and motivation for using AI.

They tested four machine-learning algorithms (Elastic Net, Random Forest, XGBoost and LightGBM) to see which factors best predicted AI dependence. Escape and social motivation for AI use, along with social anxiety, came out on top.

A network analysis then mapped how the factors relate to each other. Escape and social motivation were the most central nodes, with the highest Expected Influence indices.

The authors conclude that emotional isolation and social interaction challenges should be the focus of efforts to address mental health problems linked to AI dependence, rather than simply reducing AI use.
sources:
- peer_reviewed | Behavioral Sciences | https://doi.org/10.3390/bs16050772 | 2026-05-14
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