# A Survey on MLLMs in Education: Application and Future Directions
###### Abstract  
This survey paper examines the applications, methodologies, and future prospects of multimodal large language models (MLLMs) within the educational landscape. MLLMs, which integrate multiple data modalities such as text, images, and audio, offer innovative solutions that enhance learning experiences across various educational domains, including language acquisition, STEM education, interactive content creation, and medical training. The paper highlights how MLLMs contribute to improved engagement, personalized learning paths, and enhanced comprehension by leveraging their ability to process and generate contextually relevant content. The key findings underscore the transformative potential of MLLMs in modern education, suggesting significant improvements in both learner outcomes and pedagogical strategies. The paper also explores emerging trends and technological advancements that could shape the future of education, advocating for continued research and collaboration among stakeholders to fully harness the capabilities of MLLMs. As the integration of MLLMs into educational settings progresses, addressing ethical considerations and ensuring equitable access remain critical to maximizing their benefits. multimodal large language models (MLLMs); AI in education; educational technology (EdTech); multimodal integration in learning; computer vision in education +
Footnote †: journal: _Future Internet_  
educational settings has opened new avenues for enhancing teaching and learning processes. They offer personalized feedback, generate educational content, support language learning, and even assist in administrative tasks [1]. Advancing beyond LLMs, multimodal large language models (MLLMs) extend these capabilities by integrating multiple data modalities such as text, images, audio, and video [5]. This integration enables MLLMs to process and generate content across different formats, making them particularly valuable for educational applications that require a holistic understanding of multimodal information. For instance, MLLMs can assist in creating interactive learning materials, facilitating visual explanations of complex concepts, and supporting accessibility for learners with diverse needs [6]. The convergence of LLMs and multimodal processing represents a significant shift in educational technology, promising to further personalize and enrich learning experiences. As these models become increasingly sophisticated and accessible, understanding their potential applications and implications in education becomes crucial. This survey aims to explore the current state of MLLMs in education, examining their applications, challenges, and future directions. The main contributions of this paper are threefold: (1) an exploration of the current applications of MLLMs in education, highlighting their potential to transform traditional learning methods; (2) an analysis of the challenges and ethical considerations associated with implementing MLLMs in educational environments, such as data privacy and security; and (3) a discussion of emerging trends and future directions for MLLMs in education [7], with recommendations for further research and collaboration. The overall structure of the survey can be viewed in Figure 1. The Introduction sets the stage by placing multimodal large language models (MLLMs) into an educational context, examining their growing importance in teaching and learning. Preliminaries of MLLMs then offers a foundational exploration of their core components and underlying technologies, clarifying how they integrate multiple modalities. Applications of MLLMs in Education illustrates their current roles in enhancing adaptive learning, providing virtual tutoring, automating content creation, and streamlining educational management systems. Discussion and Future Directions addresses ethical issues, technological hurdles, and the need for improved personalization, engagement, and accessibility, suggesting pathways to strengthen MLLMs' contributions to the educational landscape.