How Achievement Emotions and Grit Affect Learners' Psychological Well-Being in AI-Based Language Education: A Bifactor Analysis
European Journal of Education, cilt.61, sa.4, 2026 (SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 61 Sayı: 4
- Basım Tarihi: 2026
- Doi Numarası: 10.1111/ejed.70873
- Dergi Adı: European Journal of Education
- Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, IBZ Online, Periodicals Index Online, Agricultural & Environmental Science Database, EBSCO Education Source, Education Abstracts, Educational research abstracts (ERA), ERIC (Education Resources Information Center), MLA - Modern Language Association Database, Public Affairs Index, MLA International Bibliography, Academic Search Ultimate (EBSCO), Social Science Premium Collection (ProQuest), Education Collection (ProQuest), Education Source Ultimate (EBSCO), Sociology Source Ultimate (EBSCO)
- Anahtar Kelimeler: achievement emotions, artificial intelligence-based contexts, grit, psychological well-being, self-determination theory
- Ankara Hacı Bayram Veli Üniversitesi Adresli: Evet
Özet
The presentation of Artificial Intelligence (AI) within educational settings has changed the conventional learning settings to advanced tools that improve the students' academic achievement. Psychological Well-being (PWB) is a critical element of students' achievement in AI-based settings. In AI-based language learning settings, achievement emotions and grit are specifically significant because they form the way students react to difficulties, engage with the learning activities and maintain motivation. While grit and achievement emotions are predictors of academic achievement, their particular roles in directing the demands of AI-mediated environments are not fully examined. From the perspective of Self-Determination Theory (SDT), the current study investigates the impact of two concepts, achievement emotions and grit, in improving learners' PWB in AI-based settings. Thus, 679 EFL students from two universities in China who actively used AI tools in their language learning participated. Three validated scales adapted to the AI-based settings were administered for data collection. The researcher used a bifactor modelling approach to examine the particular assistance of grit and achievement emotions to PWB. Utilising a bifactor modelling approach, this study examines the extent to which these constructs individually and jointly predict PWB. The results suggest that the psychological resources shared by achievement emotions and grit are more important for explaining PWB than the unique facets of each construct considered separately. Therefore, the results support the view that achievement emotions and grit function not only as distinct psychological characteristics but also as manifestations of a broader adaptive psychological disposition that is essential for flourishing in AI-based learning environments. The study also contributes to advancing the theory and recommends practical uses for creating interventions that encourage grit and emotion to grow EFL students' PWB. Theoretically, the findings expand the applicability of SDT in the AI domain and practically, it provides a framework for teachers to plan interventions that promote students' emotional and psychological health.