Utilizing Generative AI to Enhance EFL Learners’ Learning Motivation: A Case Study

Artificial intelligence (AI) has become a topic that increasingly received close review in recent years, and its impact is becoming more obvious in a variety of disciplines, including education. This study examines the integration of generative AI in Business English Class to enhance EFL learners’ motivation. A sequential explanatory mixed-methods research design was employed, involving both quantitative and qualitative data collection methods. The quantitative data were analyzed by using descriptive statistics and paired t-test. Meanwhile, the qualitative data were analyzed by using thematic analysis. Twenty-three Indonesian students who were enrolled in Business English course were involved in gathering data that were collected through questionnaires and interviews. During the eight-week experimental periods, the students engaged with the generative AI frequently. The findings of descriptive statistical tests revealed that there was a change in student motivation before and after being taught with AI. This difference indicates an increase in student interest after being taught with AI. Furthermore, the paired t-test findings suggested that the increase was classified as significant. From the interviews, it was found that EFL learners experienced a significant increase in their learning motivation derived from the autonomous and controlled motivation, particularly the contingent self-esteem. The generative AI can also be a valuable tool for enhancing EFL learners’ learning motivation by providing personalized feedback, fostering a sense of autonomy, creating a more interactive and engaging learning environment and resolving psychological barriers. Further research should explore how this technology can be scaled to broader educational settings and examine its long-term impact on learner’s motivation and language acquisition. Hence, it helps educators develop best practices for integrating AI tools effectively into the curriculum.