A Correlational Study of AI Usage, Motivation, Self‐Efficacy, and Learning Engagement in Education
Based on Expectancy‐Value Theory
Bibliographic Data
| ID | 21446535 |
|---|---|
| Authors | Qi Chai (0009-0006-1721-3100, School of Foreign Studies, Henan Agricultural University Zhengzhou Henan China, corresponding author) |
| Year | 2026 |
| Volume | 61 |
| Issue | 2 |
| Publication date | 2026-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | European Journal of Education (JOURNAL) |
| Journal identifiers | ISSN: 0141-8211 • E-ISSN: 1465-3435 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/ejed.70633 |
| OpenAlex | W7154097700 |
| Language | EN |
| References cited | 57 |
The rapid integration of artificial intelligence (AI) tools into educational settings has reshaped language learning practices; however, empirical evidence explaining how AI usage interacts with learners' motivational beliefs and self‐perceptions to influence engagement and enjoyment remains limited, particularly in EFL contexts. Although prior studies have examined technology adoption or learner motivation separately, few have situated AI‐assisted learning within a comprehensive motivational framework such as Expectancy‐Value Theory. Addressing this gap, the present study investigated the relationships among AI usage, motivation, self‐efficacy, learning engagement, and learning enjoyment among 563 Chinese EFL learners. Guided by Expectancy‐Value Theory, the study further explored the predictive roles of AI usage, motivation, and self‐efficacy in shaping learners' engagement and enjoyment. Data were collected through validated questionnaires measuring AI tool usage frequency, motivation, enjoyment, self‐efficacy, and engagement. Correlation analyses demonstrated significant positive associations among all variables. Multiple regression and structural equation modelling (SEM) analyses revealed that motivation was the strongest predictor of both engagement and enjoyment, followed by self‐efficacy and AI usage. The findings indicate that AI integration alone does not automatically enhance learning outcomes; rather, its effectiveness depends on how it aligns with learners' expectancy beliefs and task values. By extending Expectancy‐Value Theory to AI‐mediated EFL learning environments, this study contributes to the theoretical understanding of motivational processes in technology‐enhanced education and offers pedagogical implications for educators and curriculum designers aiming to foster sustained engagement and positive emotional experiences through AI‐supported instruction
Curriculum · Empirical evidence · Empirical research · Expectancy theory · Self-efficacy · Structural equation modeling · Task (project management) · Test (biology) · AI in Service Interactions · Motivation and Self-Concept in Sports · Technology-Enhanced Education Studies
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| Highly cited | No |
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