TY - JOUR
T1 - Taking the next step with generative artificial intelligence
T2 - The transformative role of multimodal large language models in science education
AU - Bewersdorff, Arne
AU - Hartmann, Christian
AU - Hornberger, Marie
AU - Seßler, Kathrin
AU - Bannert, Maria
AU - Kasneci, Enkelejda
AU - Kasneci, Gjergji
AU - Zhai, Xiaoming
AU - Nerdel, Claudia
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2025/2
Y1 - 2025/2
N2 - 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 Vision, capable of processing multimodal data including text, sound, and visual inputs, opens a new era of enriched, personalized, and interactive learning landscapes in education. This paper derives a theoretical framework for integrating MLLMs into multimodal learning. This framework serves to explore the transformative role of MLLMs in central aspects of science education by presenting exemplary innovative learning scenarios. Possible applications for MLLMs range from content creation to tailored support for learning, fostering engagement in scientific practices, and providing assessments and feedback. These applications are not limited to text-based and uni-modal formats but can be multimodal, thus increasing personalization, accessibility, and potential learning effectiveness. Despite the many opportunities, challenges such as data protection and ethical considerations become 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 educators' roles, ensuring an effective and ethical use of AI in science education. It calls for further research to explore the nuanced implications of MLLMs for educators and to extend the discourse beyond science education to other disciplines. Through developing a theoretical framework for the integration of MLLMs into multimodal learning and exploring the associated potentials, challenges, and future implications, this paper contributes to a preliminary examination of the transformative role of MLLMs in science education and beyond.
AB - 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 Vision, capable of processing multimodal data including text, sound, and visual inputs, opens a new era of enriched, personalized, and interactive learning landscapes in education. This paper derives a theoretical framework for integrating MLLMs into multimodal learning. This framework serves to explore the transformative role of MLLMs in central aspects of science education by presenting exemplary innovative learning scenarios. Possible applications for MLLMs range from content creation to tailored support for learning, fostering engagement in scientific practices, and providing assessments and feedback. These applications are not limited to text-based and uni-modal formats but can be multimodal, thus increasing personalization, accessibility, and potential learning effectiveness. Despite the many opportunities, challenges such as data protection and ethical considerations become 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 educators' roles, ensuring an effective and ethical use of AI in science education. It calls for further research to explore the nuanced implications of MLLMs for educators and to extend the discourse beyond science education to other disciplines. Through developing a theoretical framework for the integration of MLLMs into multimodal learning and exploring the associated potentials, challenges, and future implications, this paper contributes to a preliminary examination of the transformative role of MLLMs in science education and beyond.
KW - Artificial Intelligence
KW - ChatGPT
KW - Cognitive Theory of Multimedia Learning
KW - Large Language Models (LLMs)
KW - Multimodal learning
KW - Science education
UR - http://www.scopus.com/inward/record.url?scp=85214339924&partnerID=8YFLogxK
U2 - 10.1016/j.lindif.2024.102601
DO - 10.1016/j.lindif.2024.102601
M3 - Article
AN - SCOPUS:85214339924
SN - 1041-6080
VL - 118
JO - Learning and Individual Differences
JF - Learning and Individual Differences
M1 - 102601
ER -