Artificial Intelligence Addiction, Technostress, and Work-Life Balance Among Academics: An Employee Health Perspective

Aim: This study aims to examine the relationships between artificial intelligence addiction tendencies, technostress levels, and work-life balance among academics within the framework employee health.Method: This study was designed using a correlational survey model within the framework of quantitative research methods. The population of the study consisted of academics working at universities in Türkiye. Data were collected using a sociodemographic information form, the Artificial Intelligence Addiction Scale, the Technostress Scale, and the Work-Life Balance Scale. Statistical analyses were performed using SPSS for Windows 25.0 software.Results: The findings indicated that the levels of artificial intelligence addiction (11.12±4.17) and technostress (56.13±12.85) among the participating academics (n=450) were moderate, with techno-overload identified as the most prominent subdimension of technostress. The analyses revealed positive and statistically significant correlations among the variables (p<0.01). Regression analysis demonstrated that both artificial intelligence addiction and technostress significantly and positively predicted work-life balance (R²=0.315). From a sociodemographic perspective, younger academics (research assistants) exhibited higher levels of artificial intelligence addiction and work-life balance compared to professors. Additionally, a heavy teaching load of 16 hours or more per week was found to significantly increase the technostress level.Conclusion: Artificial intelligence addiction and technostress are thought to be viewed not as destabilizing risk factors for academics but rather as a “functional adaptation tool” used to manage academic workload. While this resulting “good stress state” positively supports work-life balance, implementing human-centered digital policies at the corporate level is necessary to prevent long-term cognitive fatigue.