Large multimodal foundation models (LMFMs) like ChatGPT-4-Turbo and Gemini have transformative potential in education but require careful implementation to balance their benefits with challenges regarding accuracy, ethics, privacy, and educational inequalities.
Objective: This perspective article aims to explore the new opportunities and challenges that arise from integrating large multimodal foundation models (LMFMs) in educational settings, expanding upon previous discussions of large language models (LLMs) in education.
Methods: The authors provide a comprehensive overview based on literature analysis and expert perspectives from a multidisciplinary team of researchers. They examine how LMFMs, which can process spoken text, music, images, and videos, differ from traditional LLMs and how these expanded capabilities affect their educational applications.
Key Findings:
- LMFMs offer unprecedented opportunities for personalized learning through multimedia learning experiences, barrier-free interaction for students with disabilities, and support during various learning activities
- For teachers, LMFMs can enhance lesson planning, facilitate exploration of new teaching methods, streamline administrative tasks, and support assessment creation and grading
- For researchers, LMFMs provide advanced data analysis capabilities, improved literature reviews, graphical data interpretation tools, and support for diverse research teams
- For educational software developers, LMFMs enable easier creation of intelligent learning applications, facilitate human-centered design processes, and allow collaborative development of complex software
- Major challenges include verifying the accuracy of LMFM outputs, managing potential biases, avoiding overreliance, addressing digital divide concerns, and maintaining human relationships in educational settings
- Ethical concerns arise regarding data privacy, potential perpetuation of biases, and inequitable access to these technologies
Implications: The integration of LMFMs in education represents a significant shift that could transform teaching, learning, and educational research. These technologies have the potential to make education more personalized, accessible, and effective, but require thoughtful implementation strategies. The authors emphasize the importance of keeping "humans in the loop" - maintaining the central role of teachers, researchers, and developers in guiding and overseeing LMFM use.
Limitations: The article acknowledges that LMFMs have limitations in fully meeting diverse educational needs and specific research contexts. Current models may struggle with tasks requiring comprehensive synthesis of information from multiple sources and contexts. There are also concerns about resource requirements, as advanced LMFMs often demand substantial computational and economic resources that may not be readily available to all educational institutions.
Future Directions: The authors suggest several approaches to mitigate challenges, including comprehensive AI literacy training for educators and students, transparent documentation of LMFM use in research, systematic evaluation of AI limitations, and the development of assessment strategies that align with learning objectives in LMFM-enhanced environments. They also highlight the importance of considering open-source versus proprietary LMFMs for more flexible, community-centered educational futures.
Title and Authors: "On opportunities and challenges of large multimodal foundation models in education" by Stefan Küchemann, Karina E. Avila, Yavuz Dinc, Chiara Hortmann, Natalia Revenga, Verena Ruf, Niklas Stausberg, Steffen Steinert, Frank Fischer, Martin Fischer, Enkelejda Kasneci, Gjergji Kasneci, Thomas Kuhr, Gitta Kutyniok, Sarah Malone, Michael Sailer, Albrecht Schmidt, Matthias Stadler, Jochen Weller, and Jochen Kuhn.
Published On: February 26, 2025
Published By: npj Science of Learning