Multimodal LLM Question Generation for Children's Art Engagement via Museum Social Robots

This paper proposes using social robots to enhance children's experiences in museums. Specifically, we aim to equip these social robots with multimodal large language models (MLLMs) to generate questions that engage children interactively. To achieve this, we evaluate the capabilities of LLaVA models in generating diverse and relevant questions about artworks, comparing their performance on visual questions with contextual questions. We utilize a subset of the AQUA dataset to assess both quantitative metrics and qualitative aspects of the generated questions. Additionally, we examine the models' ability to create engaging questions tailored specifically for children. We emphasize how MLLMs can generate questions that may increase enjoyment during visits, promote active observation, and enhance children's cognitive and emotional engagement with artworks. This approach aims to contribute to more inclusive and effective learning experiences in museum settings.