Comparing generative AI-supported inquiry-based and adaptive learning for enhancing English vocabulary, content understanding, and literacy in Taiwan’s CLIL science education.

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Bibliographic Details
Title:	Comparing generative AI-supported inquiry-based and adaptive learning for enhancing English vocabulary, content understanding, and literacy in Taiwan’s CLIL science education.
Authors:	Lai, Cheng-Ji¹ laicj1124@nchu.edu.tw
Source:	Educational Technology & Society. Apr2026, Vol. 29 Issue 2, p206-229. 24p.
Subject Terms:	Inquiry-based learning, Content & language integrated learning, Reading comprehension, Literacy, Instructional systems, Vocabulary, Generative artificial intelligence, Bilingual education
Geographic Terms:	Taiwan
Abstract:	While Content and Language Integrated Learning (CLIL) and generative artificial intelligence (GenAI) have received increasing attention in bilingual education, limited research has compared how different instructional frameworks operate within GenAI platforms. This study examines the effectiveness of InquiryBased Learning (IBL) and Adaptive Learning (AL) implemented through ChatGPT in enhancing fifth-grade CLIL students’ English vocabulary acquisition, content understanding, and scientific literacy in Taiwan. A quasiexperimental design involved 69 students across two groups: Experimental Group 1 (EG1, n = 34) used an IBL approach, and Experimental Group 2 (EG2, n = 35) followed an AL framework. Multimodal assessments included pre- and post-tests, PowerPoint designs, and oral presentations to evaluate students’ scientific vocabulary acquisition, content understanding, and literacy. Results indicated that EG1 significantly outperformed EG2 across outcome measures. On written post-tests, EG1 students showed stronger gains in vocabulary and content knowledge. Their PowerPoint designs demonstrated greater conceptual depth, accurate use of terminology, and more purposeful integration of visuals. Similarly, EG1’s oral presentations featured clearer scientific explanations, more fluent vocabulary use, and stronger alignment between visual and verbal elements. Qualitative analysis of rater reflections further revealed that EG1 students synthesized and communicated scientific knowledge more effectively through multimodal strategies. These findings suggest that GenAI-supported IBL offers advantages over AL in fostering bilingual learners’ cognitive engagement and scientific communication. This study contributes to the emerging literature on AI-enhanced bilingual instruction by showing how structured inquiry, supported by generative technologies, can advance the dual goals of content mastery and language development in CLIL science education. [ABSTRACT FROM AUTHOR]
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Database:	Education Research Complete

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Abstract:	While Content and Language Integrated Learning (CLIL) and generative artificial intelligence (GenAI) have received increasing attention in bilingual education, limited research has compared how different instructional frameworks operate within GenAI platforms. This study examines the effectiveness of InquiryBased Learning (IBL) and Adaptive Learning (AL) implemented through ChatGPT in enhancing fifth-grade CLIL students’ English vocabulary acquisition, content understanding, and scientific literacy in Taiwan. A quasiexperimental design involved 69 students across two groups: Experimental Group 1 (EG1, n = 34) used an IBL approach, and Experimental Group 2 (EG2, n = 35) followed an AL framework. Multimodal assessments included pre- and post-tests, PowerPoint designs, and oral presentations to evaluate students’ scientific vocabulary acquisition, content understanding, and literacy. Results indicated that EG1 significantly outperformed EG2 across outcome measures. On written post-tests, EG1 students showed stronger gains in vocabulary and content knowledge. Their PowerPoint designs demonstrated greater conceptual depth, accurate use of terminology, and more purposeful integration of visuals. Similarly, EG1’s oral presentations featured clearer scientific explanations, more fluent vocabulary use, and stronger alignment between visual and verbal elements. Qualitative analysis of rater reflections further revealed that EG1 students synthesized and communicated scientific knowledge more effectively through multimodal strategies. These findings suggest that GenAI-supported IBL offers advantages over AL in fostering bilingual learners’ cognitive engagement and scientific communication. This study contributes to the emerging literature on AI-enhanced bilingual instruction by showing how structured inquiry, supported by generative technologies, can advance the dual goals of content mastery and language development in CLIL science education. [ABSTRACT FROM AUTHOR]
ISSN:	11763647
DOI:	10.30191/ETS.202604_29(2).RP12