The Dual Forces of AI: How Generative AI and Perceived AI Dependency Influence Fear of Missing Out (FoMO) and EFL Students' Vocabulary Acquisition

Summary

This study examines how AI integration affects English learners psychologically and cognitively. It finds AI use increases anxiety (FoMO), harming reading comprehension, while digital burnout weakens the link between comprehension and vocabulary. The goal is to highlight the need for balanced AI use to support learning.

Abstract

This study examines how artificial intelligence (AI) integration in language learning influences psychological and cognitive outcomes among English as a Foreign Language (EFL) learners. Specifically, it investigates the effects of perceived AI dependency, ethical concerns, and generative AI usage on Fear of Missing Out (FoMO), reading comprehension measured by reading strategies scale, and vocabulary acquisition, while considering the moderating role of digital burnout. A cross-sectional survey design was employed with n = 450 EFL learners, and data were analyzed using partial least squares structural equation modeling (PLS-SEM) to assess both direct and mediated relationships. The findings reveal that AI-related factors significantly predict higher FoMO, which in turn negatively impacts reading comprehension. While reading comprehension strongly supports vocabulary acquisition, digital burnout weakens this relationship. The study highlights the dual nature of AI in education, facilitating learning outcomes while simultaneously contributing to psychological strain. These results underscore the importance of balanced AI integration in language learning, emphasizing the need for strategies that mitigate digital anxiety and burnout. Theoretical implications extend research on technology-mediated learning by identifying FoMO as a critical affective pathway in AI-driven educational contexts. Practical recommendations include incorporating digital wellness practices alongside AI tools to optimize learning benefits while minimizing adverse psychological effects.

Introduction

The rapid integration of Generative Artificial Intelligence (AI) into education has transformed traditional language learning paradigms, particularly in English as a Foreign Language (EFL) contexts¹. Tools such as AI-powered vocabulary assistants, automated writing feedback systems, and adaptive learning platforms promise enhanced efficiency and personalized instruction². However, this technological shift has also introduced psychological and pedagogical challenges, including perceived AI dependency, digital burnout, and ethical concerns, which may mediate learning outcomes³. One understudied yet ....

Protocol

This study was conducted in compliance with ethical guidelines for human subject's research at Yangzhou University. Informed consent was obtained from all participants prior to their involvement, ensuring they were fully informed of the study's purpose, survey-based procedures, voluntary nature, and their right to withdraw at any time without consequence. All methods adhered to ethical standards for human research, ensuring participant confidentiality, data anonymization, and protection throughout the study. The study involved non-invasive, survey-based data collection with minimal risk to participants. The study adheres to ethical guidelines for educational r....

Results

Descriptive statistics
The descriptive statistics offer an overview of the important variables in this study, i.e., perceived AI dependency, ethical concerns, and generative AI usage, Fear of missing out (FoMO), reading comprehension, digital burnout, and vocabulary acquisition. The analysis will show central tendencies, dispersion, and distributional characteristics of the data, providing an understanding of the interest of the participants in AI technologies and the psychological and reasoning resu.......

Discussion

The current study examined the complex interplay between AI-related factors, psychological states, and learning outcomes among EFL learners, yielding several key findings. Our methodological approach, specifically the use of PLS-SEM, was critical for modeling these complex relationships. Unlike covariance-based SEM (CB-SEM), which is better suited for theory confirmation, PLS-SEM's predictive orientation and ability to handle complex models with mediating and moderating variables made it the optimal choice for this e.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Laptop Computer	Dell Technologies	N/A	Used for data processing and documentation
Statistical Software (SPSS)	IBM Corp.	Version 26.0	Used for statistical analysis
Microsoft Excel	Microsoft	Office 365	Data entry and basic data handling