Predicting the Phenomenon of Dependence to Artificial Intelligence Tools based on the Components of Dependence to Mobile Phone in Students (Case Study: ChatGPT)

Abstract

Introduction and Aim: Nowadays, the use of artificial intelligence tools and mobile phones has grown significantly. Therefore, the present study was conducted with the aim of predicting the phenomenon of dependence to artificial intelligence tools (ChatGPT case study) based on the components of dependence to mobile phone in students.

Methodology: This study was a descriptive from type of correlation. The research population was all female students of Farhangian University of Kashan city in the 2025-6 academic years, which 350 people of them were selected as samples with using multistage cluster sampling method. The instruments of the present research were included the Demographic Information Form, Researcher-Made Questionnaire of Dependence to Artificial Intelligence Tools, and Cell Phone Addiction Scale (Koo, 2009). The data of this study were analyzed using Pearson correlation coefficients and multiple regression with simultaneous model in SPSS-27 software at a significance level of 0.05.

Findings: The findings of the present research showed that all components of dependence to mobile phone including withdrawal/tolerance, life dysfunction, and compulsion/persistence had a positive and significant relationship with dependence to artificial intelligence tools in students (P<0.001). Also, other findings of this study showed that the components of dependence to mobile phone were able to predict 42 percent of the changes of dependence to artificial intelligence tools in them (P<0.001).

Conclusion: According to the findings of this study, in order to reduce dependence to artificial intelligence tools, can be used the educational workshops to provide a basis for reducing dependence to mobile phone in students.