Cheng-Ji Lai

Language Center, National Chung Hsing University, Taiwan (ROC) // laicj1124@nchu.edu.tw

Vivien Lin

Graduate Institute of Technology and Adolescent English, National Changhua University of Education, Taiwan (ROC) // vivienster@gmail.com

George Martin Berry

Department of Applied English, Chaoyang University of Technology, Taiwan (ROC) // gmberr@gmail.com

Ying-Tung Lin

Graduate Institute of Technology and Adolescent English, National Changhua University of Education, Taiwan (ROC) // m1346007@gm.ncue.edu.tw

ABSTRACT: 

While immersive virtual reality (IVR) and generative AI (GenAI) each offers pedagogical potential, few studies have explored how their integration can support bilingual learners’ construction of scientific explanations in Content and Language Integrated Learning (CLIL) classrooms. This quasi-experiment investigates the effects of a pedagogical GenAI agent in IVR science review games on fifth-grade students’ CLIL science performance, as well as their perceptions on the Task-Technology Fit (TTF). Fifty CLIL learners experienced one of the three conditions: (a) IVR+GenAI review, (b) IVR-only review, and (c) teacher-led review. Data sources included a CLIL science test, oral explanations, surveys, interviews, rater reflections. Findings show that the IVR+GenAI group significantly outperformed the other groups in understanding scientific concepts, particularly for abstract and language-heavy content. For oral explanations, the IVR+GenAI group showed stronger performance in selected dimensions, particularly vocabulary usage, fluency, and communicative confidence in more abstract or language-demanding topics. Rater observations indicated clearer reasoning, more frequent integration of prior knowledge, and more explicit links between scientific concepts and real-world examples in this group. Survey responses further suggested higher perceived task–technology fit among IVR+GenAI students. This study supports the cognitive-linguistic fit in CLIL science IVR games, demonstrates the affordances of multimodal, dialogic GenAI scaffolds in IVR environments, and offers guidance for designing CLIL science IVR games.

Keywords: 

Content and language integrated learning (CLIL), Generative artificial intelligence (GenAI), Immersive virtual reality (IVR) games, Pedagogical agents, Task-technology fit (TTF), Scientific oral explanations