AI-enabled academic vocabulary learning for multilingual children

Vocabulary knowledge is fundamental to academic achievement, yet multilingual learners with English as an Additional Language or Dialect (EAL/D) consistently demonstrate vocabulary gaps compared to monolingual peers. Despite evidence supporting explicit vocabulary instruction, scalable and personalised interventions remain limited. This study evaluated an AI-enabled vocabulary learning web application designed to pre-teach academic vocabulary to EAL/D students and support curriculum content learning. The application utilises multimodal large language models (LLMs) for conversational instruction, automatic speech recognition, response evaluation, and text-to-speech production. Thirty-one multilingual learners in Grades 2–4 were recruited in Sydney, Australia. Using a counterbalanced within-subjects design, 24 target science vocabulary words were assessed at pre-test. Participants then received AI teaching for 12 of these words over six at-home sessions across 2 weeks (the remaining 12 served as untrained control), followed by a matched post-test. Over the following 2 weeks, all participants received content lessons on four science topics, two containing pre-trained vocabulary and two that did not. Comprehension was assessed via topic-specific questions. Linear mixed-effects models showed significantly AI-training effect (d = 1.45), compared to the untrained words, and significantly higher comprehension accuracy for lessons paired with trained vocabulary (d = 0.21). Students and parents viewed the application positively, particularly its personalised, multilingual feedback, while highlighting technical reliability as an area for improvement. These findings provide initial evidence that AI-enabled, curriculum-aligned vocabulary pre-teaching can support EAL/D students’ academic vocabulary development and content learning, warranting evaluation at scale in school settings.