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Taking the Next Step with Generative Artificial Intelligence: The Transformative Role of Multimodal Large Language Models in Science Education

Arne Bewersdorff, Christian Hartmann, Marie Hornberger, Kathrin Seßler, Maria Bannert, Enkelejda Kasneci, Gjergji Kasneci, Xiaoming Zhai, Claudia Nerdel

TL;DR

The paper addresses how Multimodal Large Language Models (MLLMs) can transform science education by leveraging the Theory of Multimedia Learning to support adaptive, multimodal pedagogy. It proposes an AI-enhanced framework wherein MLLMs transform content between text and visuals and add modalities to reduce cognitive load, applicable to educators and learners. The work catalogs exemplary applications across content creation, learning support, and assessment/feedback, highlighting potential benefits for personalization, accessibility, and engagement while acknowledging ethical, privacy, and bias concerns. The discussion emphasizes the educator’s evolving role and the need for careful, regulated integration, calling for further research to refine design principles and assess impact across disciplines.

Abstract

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.

Taking the Next Step with Generative Artificial Intelligence: The Transformative Role of Multimodal Large Language Models in Science Education

TL;DR

The paper addresses how Multimodal Large Language Models (MLLMs) can transform science education by leveraging the Theory of Multimedia Learning to support adaptive, multimodal pedagogy. It proposes an AI-enhanced framework wherein MLLMs transform content between text and visuals and add modalities to reduce cognitive load, applicable to educators and learners. The work catalogs exemplary applications across content creation, learning support, and assessment/feedback, highlighting potential benefits for personalization, accessibility, and engagement while acknowledging ethical, privacy, and bias concerns. The discussion emphasizes the educator’s evolving role and the need for careful, regulated integration, calling for further research to refine design principles and assess impact across disciplines.

Abstract

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.
Paper Structure (21 sections, 4 figures, 1 table)

This paper contains 21 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Review of the literature
  3. Core Elements of Science Education
  4. Large Generative AI Models
  5. Adaptive Multimodal Learning
  6. Framework of Integrating MLLM into Multimodal Learning
  7. Applications of Multimodal LLMs for Science Education
  8. MLLMs for Content Creation
  9. MLLMs for Supporting and Empowering Learning
  10. Fostering Scientific Content Knowledge Construction
  11. Fostering the Uses of Scientific Language
  12. Supporting Engagement in Scientific Practices
  13. Supporting Scientific Communication & Presentation
  14. MLLMs for Assessment and Feedback
  15. Visual Assessment
  16. ...and 6 more sections

Figures (4)

  • Figure 1: Framework of Integrating MLLM into Multimodal Learning
  • Figure 2: Example of fostering scientific content knowledge: Graphical representation of an insect's eye uploaded to an MLLM (ChatGPT with GPT4-Vision, openai2023gpt4v) asking for explanatory information for a 5th as well as a 12th grade student.
  • Figure 3: Example of fostering scientific practices: Students can ask about which steps to perform in an inquiry by uploading the given material (here, ChatGPT with GPT4-V openai2023gpt4v was used).
  • Figure 4: Example of supporting students' scientific presentations: Students can adaptively use their tabular data to create diagrams and then generate explanatory text based on these diagrams.