AUTHOR=Yan Jun , Wu Chu , Tan Xianzhen , Dai Mao 

TITLE=The influence of AI-driven personalized foreign language learning on college students’ mental health: a dynamic interaction among pleasure, anxiety, and self-efficacy

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1642608

DOI=10.3389/fpubh.2025.1642608

ISSN=2296-2565

ABSTRACT=IntroductionThis study examines the effects of AI-driven personalized foreign language learning on college students’ mental health, with a focus on the dynamic interaction among pleasure, anxiety, and self-efficacy. Drawing on cognitive load theory, self-determination theory, and dynamic systems theory, the research constructs a mental variable influence model to explore how emotional and cognitive factors shape learners’ experiences.MethodsA mixed-methods design was adopted, integrating questionnaire surveys, experimental research, and time series analysis. College students were randomly assigned to an experimental group receiving AI-driven personalized learning or a control group using traditional learning methods. The study tested hypotheses regarding the relationships among pleasure, anxiety, and self-efficacy and tracked their temporal evolution during the learning process.ResultsComparative analysis revealed that AI-driven personalized learning significantly enhanced pleasure, reduced anxiety, and strengthened self-efficacy compared with traditional methods. Pleasure and self-efficacy exerted a mitigating effect on anxiety, while heightened anxiety negatively influenced self-efficacy. Time series analysis further showed a phased pattern: after an adaptation period, pleasure and self-efficacy progressively increased, while anxiety levels demonstrated a sustained decline over time.DiscussionThe findings provide empirical insights into the interaction mechanisms among mental variables in AI-supported learning. They suggest that AI-driven systems should integrate emotional regulation mechanisms—such as adaptive feedback, personalized emotional support, and social interaction functionalities—to enhance learners’ mental experiences and sustain learning persistence. This study contributes to the optimization of AI-driven personalized learning models, supports the design of intelligent educational technologies, and strengthens mental health protection for foreign language learners.