Students who use AI to escape or socialize are most at risk of dependence, study finds

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.

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.