Evidence map›Paper›PMID 42403618›Full record

ArticleFrontiers in psychology2026

Rethinking foreign language boredom in AI-assisted learning: the hidden roles of growth mindset, AI literacy, and AI self-efficacy.

Wan Qin, Yanyan Chen, Hui Wang, Qiangwei Lu

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Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Wan QinSchool of Education, Huaibei Institute of Technology, Huaibei, China.
Yanyan ChenSchool of Education, Huaibei Institute of Technology, Huaibei, China.
Hui WangSchool of Education, Huaibei Institute of Technology, Huaibei, China.
Qiangwei LuSchool of Education, Huaibei Institute of Technology, Huaibei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: With the rapid integration of artificial intelligence (AI) into English as a foreign language (EFL) learning, learners' emotional experiences have attracted increasing attention. Among these emotions, EFL boredom, a persistent negative emotion, can substantially undermine motivation, classroom engagement, and learning outcomes. Although previous studies suggest that growth mindset may alleviate negative emotions in EFL learning, its influence on EFL boredom in AI-assisted learning environments remains underexplored. Meanwhile, AI literacy and AI self-efficacy may also play important roles. Therefore, Drawing on the frameworks of Social Cognitive Theory (SCT) and Control-value Theory(CVT), this study examined the relationships among growth mindset, AI literacy, AI self-efficacy, and EFL boredom and further constructed a sequential mediation model. Methods: A time-lagged design was adopted, involving three waves of data collection among 536 university first-year students from two higher education institutions in eastern China. Growth mindset was measured in Phase 1, AI literacy and AI self-efficacy in Phase 2, and EFL boredom in Phase 3, with two-month intervals between phases. Data were collected using validated scales. SPSS 26.0 and AMOS 21.0 were employed for descriptive statistics, correlation analysis, structural equation modeling (SEM), and Bootstrap mediation analyses. Results: The results showed that growth mindset positively predicted AI literacy and AI self-efficacy. Both AI literacy and AI self-efficacy negatively predicted EFL boredom, while AI literacy further positively predicted AI self-efficacy. However, growth mindset did not directly predict EFL boredom. In addition, AI literacy and AI self-efficacy exerted significant independent and sequential mediating effects in the relationship between growth mindset and EFL boredom. Discussion: The findings suggest that growth mindset alleviates EFL boredom indirectly through AI-related competencies and beliefs rather than through a direct pathway. The study highlights the important roles of AI literacy and AI self-efficacy in shaping emotional experiences in AI-assisted EFL learning and extends the application of growth mindset theory to AI-enhanced language education.

Indexed as

AI literacyAI self-efficacyEFL boredomEFL learninggrowth mindset

Identifiers

PMID42403618
PMCPMC13327876

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.