Pilot studies demonstrate significant vocabulary gains in young learners using AI-driven LEARN system for multilingual education, suggesting improved engagement.
Generative AI offers transformative potential for scalable, personalized, and dynamic language education, particularly in enhancing oral proficiency among young learners. However, effective deployment remains challenging due to limited resources for some languages, the need for age-appropriate content and tools, and the importance of respecting cultural relevance. In this paper, we introduce LEARN (Language Evaluation via question Answer generation from caRtooNs), a culturally grounded multilingual visual dialogue system designed to support oral proficiency in three of Singapore’s official languages: Mandarin, Bahasa Melayu, and Tamil. English, as the lingua franca, is excluded. LEARN integrates a teacher-facing module for curriculum-aligned visual question-answering task creation and a student-facing module for voice-driven adaptive dialogue, optimized for children’s speech. Unlike existing platforms, LEARN prioritizes cultural relevance and low-resource language support, helping address gaps in heritage language preservation. Pilot studies with students demonstrate significant improvements in engagement and vocabulary acquisition. Designed for classroom as well as home use, LEARN presents a scalable AI-driven language tutoring framework.
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Tushar et al. (2026) studied this question.
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