Understanding AI-Assisted EFL Learning: The Roles of Expectancy Value, AI Literacy, and Self-Regulated Learning [Abstract]

Abstract

Aim/Purpose This study investigated how learner-related factors: expectancy value, AI literacy, and self-regulated learning influence university students’ engagement in AI-assisted EFL learning environments. Background While AI-assisted language learning offers personalized and adaptive learning opportunities, its effectiveness depends on learners’ motivation, technological understanding, and ability to regulate their own learning. Methodology A survey research design was employed using a structured questionnaire developed from relevant literature and pilot tested to ensure validity and reliability. The sample consisted of 322 university students from eight universities across northern, central, southern, and eastern Taiwan. Data were analyzed using descriptive statistics, independent-samples t-tests, and one-way ANOVA. Contribution This study provides empirical evidence on how expectancy value, AI literacy, and self-regulated learning jointly shape student engagement in AI-assisted EFL contexts, extending expectancy-value theory into technology-enhanced language learning. Findings Results indicate that students reported relatively high levels of AI literacy, expectancy value, and self-regulated learning. Among sub-dimensions, AI ethics scored the highest, while resource management scored the lowest. No significant gender differences were found. However, significant differences emerged across English proficiency levels for all three variables, suggesting that perceived proficiency plays a key role in shaping motivation, AI literacy, and learning regulation. Recommendations for Practitioners Educators should design AI-assisted EFL activities that enhance learners’ motivation, develop AI literacy (especially ethical awareness), and support self-regulated learning strategies, particularly in resource management. Recommendations for Researchers Future studies should explore causal relationships among these variables, incorporate longitudinal designs, and examine how instructional interventions can strengthen AI literacy and self-regulated learning in diverse contexts. Impact on Society By improving understanding of learner factors in AI-assisted education, this study supports the development of more effective and equitable technology-enhanced language learning environments. Future Research Further research should investigate how different types of AI tools influence learning outcomes, as well as how individual differences such as proficiency, motivation, and digital competence interact over time in AI-supported learning contexts.