  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.
