Skip to content

Explaining FOMO Symptoms Based on Maladaptive Perfectionism and Emotional Loneliness with Emphasis on the Mediating Role of Artificial Intelligence Dependency among Female High School Adolescents

Oct 2026 · Journal of Adolescent and Youth Psychological Studies (JAYPS)
Impact of Technology on Adolescents

Abstract

Objective: The present study aimed to explain FOMO (Fear of Missing Out) symptoms based on maladaptive perfectionism and emotional loneliness, with a particular focus on the mediating role of artificial intelligence dependency among female high school adolescents. Methods and Materials: This correlational study employed a structural equation modeling approach to investigate the relationships among maladaptive perfectionism, emotional loneliness, artificial intelligence dependency, and FOMO symptoms. The statistical population consisted of all female students enrolled in secondary high schools in Dezful, Iran, during the 2025–2026 academic year. A total of 250 students were selected through convenience sampling. Data were collected using the Fear of Missing Out Scale developed by Przybylski et al. (2013), the Multidimensional Perfectionism Questionnaire by Hewitt and Flett (2004), the Social and Emotional Loneliness Scale by DiTommaso et al. (2004), and the Artificial Intelligence Dependency Questionnaire developed by Huang et al. (2024). Data analysis was conducted using descriptive and inferential statistics in SPSS and AMOS software packages. Path analysis and structural equation modeling were utilized to evaluate both direct and indirect relationships among the study variables and to assess the overall model fit. Findings: The results demonstrated that maladaptive perfectionism and emotional loneliness significantly predicted FOMO symptoms both directly and indirectly through artificial intelligence dependency. Maladaptive perfectionism showed a significant positive effect on artificial intelligence dependency (β = 0.411, p < .01), while emotional loneliness also exhibited a significant positive effect on artificial intelligence dependency (β = 0.506, p < .01). Furthermore, maladaptive perfectionism had a significant direct effect on FOMO symptoms (β = 0.267, p < .01) and a significant indirect effect through artificial intelligence dependency (β = 0.124, p < .01). Similarly, emotional loneliness exerted a significant direct effect on FOMO symptoms (β = 0.259, p < .01) and a significant indirect effect mediated by artificial intelligence dependency (β = 0.152, p < .01). Artificial intelligence dependency itself significantly and positively predicted FOMO symptoms (β = 0.301, p < .01). Model fit indices indicated a satisfactory fit of the proposed structural model to the observed data. Conclusion: The findings suggest that adolescents with higher levels of maladaptive perfectionism and emotional loneliness are more likely to develop dependency on artificial intelligence technologies, which in turn intensifies FOMO symptoms. Artificial intelligence dependency functions as a significant psychological mechanism linking individual vulnerabilities and social-emotional difficulties to heightened fear of missing out. These results highlight the importance of addressing maladaptive perfectionistic standards, strengthening real-world emotional relationships, and promoting healthy patterns of AI use in interventions designed to reduce FOMO among adolescents.

View source

Similar papers

#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17

KTO: Model Alignment as Prospect Theoretic Optimization

This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Nov 2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...

Masoud Mohseni, Artur Scherer, K. Johnson et al. · 121 citations · ⚡9
#artificial intelligence Review Oct 2025

Ultralytics YOLO Evolution: An Overview of YOLO26, YOLO11, YOLOv8 and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition

This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...

Ranjan Sapkota, Manoj Karkee · 112 citations · ⚡10

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.