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Arab World English Journal (AWEJ) Volume 17. Number 2, June 2026 pp. 32-48
Faculty of Education, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
Faculty of Education, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
Language Centre, Universiti Pertahanan Nasional Malaysia, Kuala Lumpur, Malaysia
Persistent difficulties in vocabulary mastery, particularly in balancing receptive and productive knowledge with long-term retention, remain a challenge among Malaysian primary learners despite ongoing curriculum reforms. This study investigated the effectiveness of the Vocabulary Flipped Classroom Model (VFCM) in improving vocabulary outcomes among Year Three Malaysian learners of English as a Second Language (ESL). The study aimed to examine whether an artificial intelligence (AI)-enhanced flipped classroom approach could significantly improve learners’ receptive vocabulary knowledge, productive vocabulary knowledge, and vocabulary retention compared with traditional teacher-centred instruction. The study employed a quasi-experimental pre-test–post-test design with a delayed post-test, involving 80 learners who were divided equally into experimental and control groups. The intervention integrated structured pre-class vocabulary tasks supported by ChatGPT, guided in-class practice, and systematic corrective feedback. Results indicated that the experimental group demonstrated significantly greater gains in both receptive and productive vocabulary, with moderate to large effect sizes, and sustained stronger retention over time than the control group. These findings highlight the pedagogical value of embedding AI-supported flipped learning within primary English classrooms to move beyond rote memorisation toward deeper lexical understanding and durable learning. The study contributes empirical evidence to current discussions on technology-enhanced language instruction and offers a scalable instructional framework for teachers, curriculum planners, and policymakers seeking sustainable vocabulary development strategies.
Ahmad Radzuan, F.A., Mohd Arif, M., & Ab. Rahman, N.K. (2026). Artificial Intelligence in Flipped Classrooms for Enhancing Vocabulary Acquisition and Retention in Primary ESL Education. Arab World English Journal, 17(2):32-48
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Farhana Atiqah Ahmad Radzuan is currently a Ph.D. candidate in TESL at the Faculty of Education, Universiti Teknologi MARA, Malaysia. Her research focuses on technology-enhanced vocabulary instruction, flipped classroom pedagogy, and artificial intelligence in ESL education. Her research examines the use of AI tools such as ChatGPT to improve vocabulary acquisition, retention, and learner engagement among primary ESL learners. ORCID: https://orcid.org/0009-0006-6546-6225
Marina Mohd Arif is a senior lecturer at the Faculty of Education, Universiti Teknologi MARA, Malaysia. Her research specialises in language teaching methodologies, teacher education, and technology-enhanced learning. She has supervised postgraduate research and published in the areas of TESL and educational technology. Her work focuses on improving teaching effectiveness through innovative pedagogies and digital tools. ORCID: https://orcid.org/0009-0004-2720-5193
Nur Khadirah Ab. Rahman is an English Lecturer at the Language Centre, Universiti Pertahanan Nasional Malaysia. Her research areas include Technology-Enhanced Language Learning, Mobile-Assisted Language Learning, and English for Specific Purposes. Her work focuses on digital learning environments, learner autonomy, and language learning for professional contexts.
ORCID: https://orcid.org/0000-0001-6118-6636