Recent studies show that large language models (LLMs) are powerful tools for working with natural language, bringing advances in many areas of computational linguistics. However, these models face challenges when applied to low-resource languages due to limited training data and difficulty in understanding cultural nuances. Research is now focusing on multilingual models to improve LLM performance for these languages. Education in these languages also struggles with a lack of resources and qualified teachers, particularly in underdeveloped regions. Here, LLMs can be transformative, supporting innovative methods like community-driven learning and digital platforms. This paper discusses how LLMs could enhance education for low-resource languages, emphasizing practical applications and benefits.
Pedagogical Alignment for Vision-Language-Action Models: A Comprehensive Framework for Data, Architecture, and Evaluation in Education
Chosun University（全州大学）；Seoul National University（首尔国立大学）；Korea Institute for Curriculum and Evaluation（韩国课程评价院）；Nanyang Technological University（南洋理工大学）
VisionUnite: A Vision-Language Foundation Model for Ophthalmology Enhanced with Clinical Knowledge
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Washington（华盛顿大学）；Shenzhen Institutes of Advanced Technology（深圳先进技术研究所）；Chinese Academy of Sciences（中国科学院）；State Key Laboratory of Ophthalmology（眼科学国家重点实验室）；Zhongshan Ophthalmic Center（中山眼科中心）；Sun Yat-sen University（中山大学）；Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science（广东省眼科学与视觉科学重点实验室）；Guangdong Provincial Clinical Research Center for Ocular Diseases（广东省眼科临床研究中心）；Department of Bioengineering（生物工程系）；Department of Radiology（放射科）
MUSE: Benchmarking Large Vision-Language Models on Multi-Modal Understanding in Situated Education
AI Singapore, National University of Singapore（新加坡国立大学新加坡人工智能）；School of Computing, National University of Singapore（新加坡国立大学计算学院）；Institute of Advanced Intelligence and Computing, A*STAR（新加坡科技研究局先进智能与计算研究所）
Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational Videos
TIB – Leibniz Information Centre for Science and Technology（蒂宾根-莱比锡信息科学与技术研究中心）；Hochschule Hannover – Data（汉诺威高等学院-数据）；H Institute for Applied Data Science（汉诺威应用数据科学研究所）；L3S Research Center – Leibniz University Hannover（L3S研究中心-汉诺威莱比锡大学）
Evaluating Multimodal Large Language Models on Educational Textbook Question Answering
Faculty of Computing and Information Technology（计算与信息科技学院）；King Abdulaziz University（阿卜杜勒阿齐兹大学）；FAST School of Computing（快速计算学院）；National University of Computer and Emerging Sciences（国家计算机与新兴科学大学）；School of Computing（计算学院）；Macquarie University（麦考瑞大学）；Center of Research Excellence in AI and Data Science（人工智能与数据科学卓越研究中心）
