ORIGINAL RESEARCH article
Front. Public Health
Sec. Public Mental Health
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1642608
This article is part of the Research TopicAdvances in Artificial Intelligence Applications that Support Psychosocial HealthView all 8 articles
The Influence of AI-Driven Personalized Foreign Language Learning on College Students' Mental Health: A Dynamic Interaction among Pleasure, Anxiety, and Self-Efficacy
Provisionally accepted
- 1School of Foreign Languages, Yancheng Teachers University, Yancheng, China
- 2School of Public Administration, Guangzhou University, Guangzhou, China
- 3School of Business, Macau University of Science and Technology, Taipa, Macao, SAR China
- 4Department of Education, Sehan University, Yeongam County, Republic of Korea
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
This study investigates the effects of AI-driven personalized foreign language learning on college students' mental health, focusing on the dynamic interaction among pleasure, anxiety, and self-efficacy. Drawing on cognitive load theory, self-determination theory, and dynamic systems theory, a mental variable influence model is constructed, integrating questionnaire surveys, experimental research, and time series analysis to examine the proposed hypotheses. A comparative analysis between an experimental group engaged in AI-driven personalized learning and a control group following traditional learning methods reveals that AI-driven personalized learning significantly enhances pleasure, reduces anxiety, and strengthens self-efficacy. Furthermore, both pleasure and self-efficacy demonstrate a mitigating effect on anxiety, while heightened anxiety levels exert a negative impact on self-efficacy. Time series analysis further identifies a phased pattern in the emotional regulation effects of AI-driven personalized learning, with pleasure and self-efficacy progressively increasing after an adaptation period, accompanied by a sustained decline in anxiety levels over time. These findings offer empirical insights into the interaction mechanisms among mental variables and contribute to the optimization of AI-driven learning system design. To enhance learners' mental experiences and promote learning persistence, AI-driven learning systems should integrate emotional regulation mechanisms, including adaptive feedback, personalized emotional support, and social interaction functionalities. This study advances the development of intelligent educational technology, refines AI-driven personalized learning models, and strengthens mental health support for foreign language learners.
Keywords: personalized learning, foreign language learning, Mental Health, Pleasure, Self- efficacy
Received: 06 Jun 2025; Accepted: 25 Aug 2025.
Copyright: © 2025 Yan, Wu, Tan and Mao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dai Mao, Department of Education, Sehan University, Yeongam County, Republic of Korea
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.