Beyond AI Use: Examining The Predictive Quality of AI Dependency & Distress Intolerance in Avoidance Coping Among Young Adults in Mumbai

Abstract Background: As the reliance on AI keeps growing, it becomes important to understand its psychological implications. The study contributes to the growing literature on AI and mental health by investigating the predictive quality of AI dependency and distress tolerance in the use of avoidance coping in response to stressful situations. Aims: The present research aims to examine the predictive relationship between AI dependency, distress intolerance and avoidance coping among young adults in Mumbai. Method: 90 participants from Mumbai (60 Females, 29 Males) who were aged between 18-25 years (M=20.8, SD=2.38) and used AI apps like ChatGPT and Gemini, were selected using convenience sampling. All participants completed the Generative AI Dependency Scale (Goh et al., 2025), Distress Tolerance Scale (Simons & Gaher, 2005) and Ways of Coping Scale-Revised (Folkman & Lazarus, 1985). Correlation and simple linear regression was computed for statistical analysis. Results: Findings of the research indicate that AI dependency showed a significant positive correlation with avoidance coping (p < .01) and hence was a statistically significant predictor. Furthermore, distress intolerance showed a weak correlation with avoidance coping and was therefore not a statistically significant predictor (p > .05) of avoidance coping. Conclusion: The findings of this study highlight the predictive role of AI dependency in the use of avoidance coping when faced with stressful situations. The role of distress tolerance in avoidance coping needs to be studied further in a more diverse and larger population. The findings may help develop strategies that promote healthy coping skills in stressful situations in the era of increasing AI use.