AI-Enhanced CLIL for Embodied Learning: Applying the CLPS Framework in Secondary Physical Education

This study examines how Artificial Intelligence (AI) can enhance Content and Language Integrated Learning (CLIL) through embodied, multimodal instruction in secondary Physical Education (PE). Drawing on Fernández Fontecha’s Content and Language Processing Sequence (CLPS) model, four AI-supported CLIL modules were designed and partially implemented in a Spanish secondary school. The exploratory, design-based study involved 25 students (aged 13–14) enrolled in second-year secondary education (2° ESO). Data were collected through a student perception survey and structured teacher observations to examine learners’ perceived content understanding, language use, engagement, and embodied participation in AI-supported CLIL tasks. Results indicate high levels of student engagement and positive perceptions of learning, particularly regarding vocabulary use, task comprehension, and the integration of physical movement with language use. Students reported that AI tools such as NaturalReader and Gliglish supported pronunciation practice, comprehension, and interactive language use when embedded within guided CLIL tasks. The findings highlight the pedagogical potential of AI as a mediating scaffold in embodied CLIL contexts, while underscoring the importance of teacher guidance and task design. The study contributes to emerging research on AI-enhanced CLIL by offering empirically grounded insights into the affordances and limitations of integrating AI in Physical Education.