Motivation and Achievement in EFL: The Power of Instructional Approach

Maintaining learning motivation and achieving academic success in English language learning remains a challenge for many university students, particularly those with lower proficiency. Conventional teacher-centered classrooms are often characterized by passive learners with limited personalized support. In contrast, blended and artificial intelligence (AI)-assisted learning have emerged as promising alternatives to address motivational and performance challenges in English as a foreign language (EFL) contexts. However, empirical comparisons of these instructional approaches remain limited. Grounded in Self-Determination Theory (SDT) and cognitive constructivism, this study examined the comparative effects of conventional, blended, and AI-blended instructional approaches on Chinese university students' goal orientation, self-efficacy, instructional support, and English academic achievement. The AI-blended approach integrated tools such as automated writing evaluation (AWE), automated speech recognition (ASR), and the chatbot DouBao to support pre-class learning. A 1.5-year longitudinal within-subject design was employed with 43 first-year EFL students at a Chinese university. Participants experienced all three instructional approaches sequentially, with data collected via motivational questionnaires and achievement tests. Repeated measures analyses, including ANOVA and Friedman tests, were conducted. Results indicated that both blended and AIblended instruction significantly improved students' motivation and academic performance relative to conventional instruction. The AI-blended approach produced the most substantial gains in self-efficacy, instructional support, and key language skills such as listening comprehension, translation, and writing. These findings inform ongoing discussions on the integration of AI in EFL pedagogy and provide practical implications for instructional design, teacher preparation, and education policy innovation. The study's limitations, including the small sample size, limited demographic diversity, and constraints of a within-subject design, should be addressed in future research.