Profiles and Transitions of Psychological Adaptation in AI-Assisted Japanese Language Learning

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Yuanyuan Wu (2026) examined how learners psychologically adapt to AI-assisted Japanese language learning, using a three-wave longitudinal survey of 457 learners across one academic semester with technostress and psychological resilience as indicators in latent profile and latent profile transition analyses. Three profiles were consistently identified — maladaptive adaptation, moderate adaptation, and positive adaptation — with the maladaptive group shrinking from 26.48% to 17.74% and the positive group growing from 27.35% to 35.73% across the semester. The positive-adaptation profile showed the greatest temporal stability, while maladaptive learners most commonly transitioned to moderate adaptation. At Time 3, positively adapting learners reported the highest Japanese learning Self Efficacy and the lowest learning burnout, with the reverse pattern for maladaptive learners, demonstrating that psychological adaptation to AI-assisted language learning is heterogeneous, dynamic, and meaningfully tied to confidence and burnout.

Key Findings

Three stable psychological-adaptation profiles: Learners were consistently classifiable into maladaptive, moderate, and positive adaptation profiles across all three waves, indicating a conceptually stable underlying pattern of adaptation to AI-assisted study.
Adaptation is dynamic, not fixed: The maladaptive profile decreased from 26.48% to 17.74% while the positive profile increased from 27.35% to 35.73% over the semester; positive-adaptation learners were most stable, and maladaptive learners most commonly shifted to moderate adaptation.
Profiles predict confidence and burnout: At Time 3, positively adapting learners reported the highest Japanese learning self-efficacy and lowest burnout; maladaptive learners showed the reverse — profiles are practically meaningful for learning outcomes.
Technostress declined and resilience rose: All five technostress dimensions declined from Time 1 to Time 3 while psychological resilience rose modestly (2.03 to 2.19), consistent with a gradual easing of technological strain as learners gained experience.
Sample context: 457 learners at Time 1 (76.1% female), mostly enrolled in non-major Japanese courses (56.0%) with intermediate proficiency (45.7%); 62.1% had no prior AI training, and the most common AI-use frequency was 3–4 times per week.

Implications for AI in Education

The study brings a person-centered, longitudinal lens to AI-supported language education, showing that learners do not respond uniformly to AI tools and that adaptation is best understood as a demand–resource balance under the Job Demands–Resources framework rather than a single continuum. For language instructors and learning designers it implies that AI-mediated environments should support learners whose technostress outweighs their resilience, and that fostering confidence and regulating self-regulated use of AI can shift learners toward positive adaptation over time. The findings also caution that perceived benefits of AI-assisted learning do not eliminate the psychological costs of managing overload, information density, and dependence on intelligent tools.

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Citation

Wu, Y. (2026). Profiles and transitions of psychological adaptation in AI-assisted Japanese language learning. Frontiers in Psychology, 17, 1837484.

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