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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

arXiv:2401.00832v3cs.AIcs.CY

TL;DR

The paper addresses how MLLMs can support the inherently multimodal demands of science education beyond text-based systems. Grounded in multimedia-learning theory, it proposes an adaptive framework and exemplary scenarios, while concluding that responsible use must preserve educator involvement and address ethical and implementation risks.

  • Problem

    Science education is inherently multimodal, but conventional learning materials and systems offer limited adaptive support across representations and learner needs.

  • Method

    The paper grounds an MLLM integration framework in multimedia-learning theory and illustrates applications for adaptive multimodal learning, assessment, and feedback.

  • Results

    MLLMs can adaptively transform, supplement, and simplify multimodal representations to support learners’ selection, organization, and integration of information.

  • Takeaways & Limitations

    MLLMs may support personalized and inclusive science learning, but should complement rather than replace educators.

  • Takeaways & Limitations

    Effective MLLM use remains constrained by guidance needs, cognitive-load risks, biased or fabricated outputs, and the evolving role of educators.

Abstract

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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.

1 INTRODUCTION

Science learning is inherently multimodal, and multimodal representations can support coherent mental models and stronger science learning. The paper positions MLLMs as tools for expanding adaptive, personalized, and multimodal science education while requiring attention to ethical and data-protection challenges.

  • Science education requires students to acquire knowledge, engage in scientific practices, and communicate scientific findings through multimodal activities.
  • Combining representations such as text and images can improve knowledge acquisition by supporting coherent, multifaceted mental models.
  • MLLMs process and generate text, images, videos, and audio, enabling applications from content creation to problem-solving.
  • The paper develops an AI-enhanced multimodal learning framework and presents scenarios for instructional design, engagement, assessment, feedback, and adaptive support.

2 REVIEW OF THE LITERATURE

The literature review connects science education’s multimodal demands with MLLMs’ expanding ability to process and transform diverse representations. It grounds adaptive multimodal learning in multimedia-learning theory and frames MLLMs as tools for selecting, organizing, and integrating information.

  • Science education combines scientific knowledge, inquiry, practical skills, communication, assessment, and feedback as central educational tasks.
  • MLLMs extend LLMs by processing and generating text, images, audio, and video, although fully integrated any-to-any input and output remains nascent.
  • Multimedia-learning theory holds that learners select, organize, and integrate verbal and visual information into coherent mental models.
  • 2.3 Adaptive Multimodal Learning: MLLMs can adapt representations in real time by converting text into mind maps, diagrams, or other visuals and extracting terms from complex scientific representations.
  • 2.3 Adaptive Multimodal Learning: The review proposes MLLMs as a framework-grounded means of adapting multimodal representations to learners’ needs during science teaching and learning.

3 FRAMEWORK OF INTEGRATING MLLM INTO MULTIMODAL LEARNING

The proposed framework places an MLLM between verbal and non-verbal channels to transform representations and add modalities according to user needs. It is an initial draft intended for further refinement.

  • The framework positions the MLLM between textual and visual channels, where it processes inputs according to users’ adaptive and personalization requirements.
  • MLLMs can transform content between text and images, such as converting tabular data into visual diagrams.
  • Adding a modality supplements text with visuals or visuals with text to reduce cognitive load and support more complete, less ambiguous mental models.
  • Educators or learners can operate the MLLM, with adaptivity depending on information about competencies, needs, and difficulties.
  • The framework is an initial draft that requires further development and refinement.

4 APPLICATIONS OF MULTIMODAL LLMS FOR SCIENCE EDUCATION

The applications section is organized around central aspects of science education and presents exemplary MLLM potentials for adaptive, multimodal teaching and learning. The examples are summarized in Table 1.

  • The paper presents exemplary MLLM applications for science teaching and learning, organized around central aspects of science education.
  • The examples focus specifically on adaptive, multimodal learning and are discussed using the proposed framework.
  • The applications are succinctly represented in Table 1.

4.1 MLLMs for Content Creation

MLLMs can help educators create tailored, multimodal science materials that accommodate diverse learners, reduce cognitive load, and support active engagement. They also enable transformations across modalities and integration into virtual-reality environments.

  • 4.1 MLLMs for Content Creation: MLLMs can create tailored multimodal learning materials that meet diverse students’ needs while supporting motivation and conceptual understanding.Multiple representations, such as diagrams, can help learners visualize concepts and develop mental models.
  • 4.1 MLLMs for Content Creation: MLLMs can transform text, tables, and other data structures into visuals or explanatory text to support scientific communication.The framework describes modality shifts such as transforming tabular data into diagrams and augmenting visuals with text.
  • 4.1 MLLMs for Content Creation: MLLMs can provide personalized assessment of text and visual reports, including multimodal feedback with visual aids.The proposed framework highlights improved assessment quality and instant feedback across texts and drawings.
  • 4.1 MLLMs for Content Creation: Organizing content and adding visual explanations can reduce cognitive load and improve accessibility for learners with different needs.Examples include step-by-step visualizations of abstract SN1 and SN2 organic chemistry mechanisms.
  • 4.1 MLLMs for Content Creation: Generative activities can shift part of content creation to students, who adaptively organize descriptions into figures, diagrams, animations, or synthetic data.This approach promotes active engagement with multimodal representations.
  • 4.1 MLLMs for Content Creation: MLLM-based code and content generation can support the design and integration of immersive virtual-reality learning environments.Spatial reasoning and APIs facilitate extending MLLMs into virtual settings.

4.2 MLLMs for Supporting and Empowering Learning

MLLMs can support science learning by enriching real-world materials, scientific practices, and communication with adaptive multimodal assistance. Examples include visual and textual explanations, inquiry guidance, data interpretation, equipment support, transcription, and storyboard generation.

  • 4.2 MLLMs for Supporting and Empowering Learning: MLLMs can supplement text-based real-world materials with visuals, helping students visualize complex concepts and access sources adapted to their backgrounds.An insect-eye example illustrates adaptive interaction with a Wikipedia image and accompanying text.
  • 4.2 MLLMs for Supporting and Empowering Learning: MLLMs can support scientific practices by helping students formulate questions and hypotheses, interpret diagrams, and plan investigations.They can provide adaptive learning materials in a second modality.
  • 4.2 MLLMs for Supporting and Empowering Learning: MLLMs can render raw data into diagrams and add contextual explanations that support interpretation and the derivation of conclusions.The visual representation is paired with textual information to bridge raw data and meaningful interpretation.
  • 4.2 MLLMs for Supporting and Empowering Learning: Students can upload laboratory materials or equipment images and receive responsive guidance during inquiry and equipment use.This support addresses technical terminology, unfamiliar instruments, and handling difficulties.
  • 4.2 MLLMs for Supporting and Empowering Learning: MLLMs can transcribe spoken words into summaries to reduce the note-taking burden during scientific inquiry.The authors caution that scaffolding should not lead students to outsource thinking or mindlessly follow procedures.
  • 4.2 MLLMs for Supporting and Empowering Learning: MLLMs can transform tabular data into diagrams and explanatory text, and generate image-based storyboards through successive textual and visual representations.The storyboard process moves from text-based analogies to static visuals and then dynamic visual analogies.

4.3 MLLMs for Assessment and Feedback

MLLMs extend science assessment and feedback beyond text by analyzing and generating multimodal content. They can support personalized evaluation and responsive visual feedback for students’ written and visual work.

  • 4.3.1 Visual Assessment: Visual assessment is constrained by the time and effort required to evaluate complex, incomplete, or contradictory student representations.Existing LLM assessment tools largely provide textual information from textual input, limiting assessment in multimodal science education.
  • 4.3.1 Visual Assessment: Students can transform tabular data into diagrams and generate explanatory text from those diagrams for scientific presentations.The scenario illustrates adaptive movement from structured data to visual and explanatory representations.
  • 4.3.1 Visual Assessment: MLLMs can assess students’ written reports together with visual content such as graphs, diagrams, and experimental drawings.This supports more comprehensive assessment of students’ cognitive processes and their ability to communicate science across written and visual modes.
  • 4.3.2 Multimodal Feedback: MLLM feedback can combine textual responses with visual aids, such as models of atoms, molecules, and reactions, to address misconceptions.These tailored figures can be generated responsively rather than stored and manually annotated for particular misconceptions.
  • 4.3.2 Multimodal Feedback: MLLMs can provide near-immediate, personalized feedback on texts and drawings, enabling students to adjust work before pursuing ineffective approaches.Feedback may be directed to students for reflection or educators for refinement and validation, and can augment text with diagrams.

5 CHALLENGES AND RISKS OF MLLMS IN SCIENCE EDUCATION

MLLMs offer adaptable multimodal support in science education but introduce risks involving guidance, reliability, ethics, regulation, access, and model governance. The paper therefore emphasizes educator oversight and responsible institutional implementation.

  • Guidance and educator roles: Minimal guidance and excessive modality choices can distract learners, increase cognitive load, and hinder learning, especially for students with low self-regulation.Educators remain pivotal in guiding MLLM use so that technology enhances rather than impedes learning.
  • Guidance and educator roles: MLLMs should provide educator-adjustable degrees of guidance or scaffolding matched to students’ competency levels.This design supports scientific practices and open inquiry without treating the model as an unrestricted adaptive agent.
  • Ethical and validity risks: MLLMs may generate biased, incorrect, faulty, or fabricated content, creating risks for assessment validity and potentially enlarging score differences by gender or English proficiency.The paper calls for empirical examination of bias in MLLM-supported assessment.
  • Ethical and validity risks: Education stakeholders must address privacy, consent, bias, equity, and data-handling risks through awareness, safeguards, and ethical governance.Responsible use requires attention to both the moral implications of MLLMs and the security of educational data.
  • Regulation and implementation: The European AI Act treats MLLM use in education as high-risk and requires disclosure, safeguards against illegal content, training-data summaries, and registration.Developers, educators, administrators, policymakers, and governance frameworks all have responsibilities for regulatory compliance.
  • Regulation and implementation: Effective implementation requires access to MLLMs alongside AI literacy and the competencies needed to use them successfully.Stakeholder skepticism may otherwise hinder classroom adoption.
  • Model governance: Proprietary MLLMs may provide data security and professional support but can restrict accessibility, customization, transparency, and cost-effective adaptation.These constraints may limit alignment with diverse educational contexts and hinder collaboration and innovation.

6 DISCUSSION

The discussion frames MLLMs as adaptive systems for transforming and organizing multimodal scientific representations. Their promise depends on guided integration that supports learners’ modality selection, organization, and information integration.

  • Adaptive transformation: MLLMs can analyze and transform information across modalities, including converting text into visual representations and simplifying difficult images or text.The paper calls this capability “adaptive transformation” and grounds its framework in the Theory of Multimedia Learning.
  • Adaptive transformation: MLLMs can provide adaptive learner-generated support, including text summaries, highlighted passages, similarity displays, and optional selection aids.These supports can be introduced when learners need them rather than being fixed in learning materials.
  • Multimodal science learning: Because science learning requires shifting among diagrams, tables, texts, images, and animations, MLLMs could support both modality-specific competence and transitions between modalities.This addresses the representational demands of interpreting diagrams, analyzing datasets, and synthesizing multiple sources.
  • Implications and future research: Adaptive design options can help learners select, organize, and integrate information according to prior knowledge and skill.Learners with more prior knowledge may choose complex representations, while less-skilled learners can adapt materials to their needs.
  • Implications and future research: The effective regulation of learner-directed or scripted MLLM use remains an open area for future research.The discussion identifies adaptive multimodal learning as promising while emphasizing unresolved questions about regulating generative learning processes.

7 FUTURE IMPLICATIONS

MLLMs may make science learning more interactive, adaptive, and multimodal, while requiring continued research and educator-centered implementation. Their broader adoption depends on recognizing that they should complement rather than replace educators.

  • 7 FUTURE IMPLICATIONS: MLLMs can combine interactivity, real-time content generation, and multimodal learning to support more individualized science learning experiences.They can transition between learning modes upon request and potentially optimize cognitive resource use while supporting learning.
  • 7 FUTURE IMPLICATIONS: Seamless transitions between learning modes may enhance learners’ representational competencies, which support deeper understanding of scientific concepts.
  • 7 FUTURE IMPLICATIONS: MLLMs could make learning environments more responsive to individual student needs and enhance the overall learning experience.
  • 7 FUTURE IMPLICATIONS: Further research should examine personalization, inclusiveness, and how increasing multimodality may shift the educator’s role.The paper states that MLLMs are still in their infancy and that educators’ roles vary with the degree of automation.
  • 7 FUTURE IMPLICATIONS: MLLMs should alter and support educators’ roles rather than replace educators or compete with them.The paper calls for a thoughtful, collaborative approach that keeps the human-centered aspects of teaching and learning visible.
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