AI-powered personalized learning: Enhancing self-efficacy, motivation, and digital literacy in adult education through expectancy-value theory

Although the implementation of artificial intelligence (AI) in educational contexts has gained increasing prominence, empirical research specifically examining its influence on crucial learner-related variables (e.g., self-efficacy, motivation, and digital literacy) among adult male learners of English as a Foreign Language (EFL) in China remains limited. The present study addresses this gap by investigating the effects of AI-powered personalized learning interventions on these key constructs. A total of 183 intermediate-level Chinese male EFL learners were randomly assigned either to an experimental group (EG), which received AI-personalized instruction, or to a control group (CG), which engaged in traditional instruction methods. Data were gathered through pre- and post-intervention surveys and analyzed using independent t-tests. Results indicated that compared to participants in the CG, learners in the EG exhibited statistically significant improvements in self-efficacy, motivation, and digital literacy. These findings offer robust empirical evidence supporting the effectiveness of AI-personalized instructional strategies in enhancing essential learner attributes within the adult male EFL context in China. Thus, the study advocates for the strategic integration of AI-powered personalized learning, highlighting its considerable potential to optimize language learning outcomes within adult EFL education.