Comparative Analysis of AI-Generated and Human-Made Instructional Videos: Effects on Learning English Caused-Motion Constructions by Korean High School EFL Learners
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This study examines the instructional efficacy and learner perceptions of AI-generated videos for teaching English causedmotion constructions and compares them with human made ones within the Korean EFL context. Employing generative AI to automate the creation of multimodal educational materials is expected to maximize instructional efficiency within Korean EFL settings. However, empirical research comparing AI-generated materials to traditional human-instructed content in language learning remains limited. This study addresses this gap using a mixed-methods design involving ninety high school students divided into the AI-generated and human-made video groups. Participants completed a perception survey and Elicited Writing Tasks (EWT) to assess engagement and learning outcomes. The results from the perception study demonstrate that Human-made videos were preferred for pacing and instructor support, highlighting the importance of human presence in instructional content. Nevertheless, AI-generated videos were also valued for their engaging visuals and efficiency, though participants suggested enhancements in avatar realism and speech clarity. A repeated-measures ANOVA revealed that both AI-generated and human-made videos significantly improved learners’ production of caused-motion constructions in the immediate posttest; however, the instructional effect was not sustained in the delayed posttest. The findings support using AI-generated videos as effective instructional tools and emphasize the need to balance technological efficiency with human-like features for optimal learning experiences. The pedagogical implications of the study are also discussed.
Abstract
This study examines the instructional efficacy and learner perceptions of AI-generated videos for teaching English causedmotion constructions and compares them with human made ones within the Korean EFL context. Employing generative AI to automate the creation of multimodal educational materials is expected to maximize instructional efficiency within Korean EFL settings. However, empirical research comparing AI-generated materials to traditional human-instructed content in language learning remains limited. This study addresses this gap using a mixed-methods design involving ninety high school students divided into the AI-generated and human-made video groups. Participants completed a perception survey and Elicited Writing Tasks (EWT) to assess engagement and learning outcomes. The results from the perception study demonstrate that Human-made videos were preferred for pacing and instructor support, highlighting the importance of human presence in instructional content. Nevertheless, AI-generated videos were also valued for their engaging visuals and efficiency, though participants suggested enhancements in avatar realism and speech clarity. A repeated-measures ANOVA revealed that both AI-generated and human-made videos significantly improved learners’ production of caused-motion constructions in the immediate posttest; however, the instructional effect was not sustained in the delayed posttest. The findings support using AI-generated videos as effective instructional tools and emphasize the need to balance technological efficiency with human-like features for optimal learning experiences. The pedagogical implications of the study are also discussed.
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