Work

Retrieval-Augmented Vision–Language Agents for Child-Centered Encyclopedia Learning

Year: 2025

Type: article

Abstract: This study introduces an Encyclopedic Agent for children’s learning that integrates multimodal retrieval with retrieval-augmented generation (RAG). To support this framework, we construct a dataset of 9524 Wikipedia pages covering 935 encyclopedia topics, each converted into images with associated topical queries and explanations. Based on this dataset, we fine-tune SigLIP, a vision–language retrieval model, using LoRA adaptation on 8484 training pairs, with 1040 reserved for testing. Experimental results show that the fine-tuned SigLIP significantly outperforms baseline models such as ColPali in both accuracy and latency, enabling efficient and precise document-image retrieval. Combined with GPT-5 for response generation, the Encyclopedic Agent delivers illustrated, interactive Q&A that is more accessible and engaging for children compared to traditional text-only methods. These findings highlight the feasibility of applying multimodal retrieval and RAG to educational agents, offering new possibilities for personalized, child-centered learning in domains such as science, history, and the arts.

Source: Applied Sciences

DOI: 10.3390/app151910821

Authors: Jing Du, Wenhao Liu, Jingyi Ye, Dibin Zhou, Fuchang Liu

Institutions: Kangwon National University, Hangzhou Normal University, Zhejiang University

Language: English

Topic: Multimodal Machine Learning Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $2,896