The integration of Artificial Intelligence (AI), particularly Large Language Model (LLM)-based systems, in education has shown promise in enhancing teaching and learning experiences. However, the advent of Multimodal Large Language Models (MLLMs) like GPT-4 with vision (GPT-4V), capable of processing multimodal data including text, sound, and visual inputs, opens a new era of enriched, personalized, and interactive learning landscapes in education. Grounded in theory of multimedia learning, this paper explores the transformative role of MLLMs in central aspects of science education by presenting exemplary innovative learning scenarios. Possible applications for MLLMs could range from content creation to tailored support for learning, fostering competencies in scientific practices, and providing assessment and feedback. These scenarios are not limited to text-based and uni-modal formats but can be multimodal, increasing thus personalization, accessibility, and potential learning effectiveness. Besides many opportunities, challenges such as data protection and ethical considerations become more salient, calling for robust frameworks to ensure responsible integration. This paper underscores the necessity for a balanced approach in implementing MLLMs, where the technology complements rather than supplants the educator's role, ensuring thus an effective and ethical use of AI in science education. It calls for further research to explore the nuanced implications of MLLMs on the evolving role of educators and to extend the discourse beyond science education to other disciplines. Through the exploration of potentials, challenges, and future implications, we aim to contribute to a preliminary understanding of the transformative trajectory of MLLMs in science education and beyond.

翻译：人工智能（AI），特别是基于大语言模型（LLM）的系统，在教育领域的整合已展现出增强教学体验的潜力。然而，能够处理文本、声音和视觉输入等多模态数据的多模态大语言模型（MLLMs），如具备视觉能力的GPT-4（GPT-4V），开启了教育领域丰富化、个性化及互动式学习的新纪元。基于多媒体学习理论，本文通过展示典型创新学习场景，探讨了MLLMs在科学教育核心方面的变革作用。MLLMs的可能应用范围涵盖从内容创作到个性化学习支持、培养科学实践能力以及提供评估与反馈。这些场景不仅限于基于文本的单模态形式，还可以是多模态的，从而提升个性化、可访问性及潜在学习效果。除诸多机遇外，数据保护与伦理考量等挑战也日益凸显，亟需建立稳健框架以确保负责任的整合。本文强调在实施MLLMs时需要采用平衡方法，使技术成为教育者角色的补充而非替代，从而确保AI在科学教育中的有效且合乎伦理的应用。文章呼吁进一步研究，以探索MLLMs对教育者角色演变的细微影响，并将讨论范围从科学教育拓展至其他学科。通过探讨潜力、挑战及未来影响，我们旨在为理解MLLMs在科学教育及其他领域的变革轨迹提供初步认识。