From MOOC to MAIC: Reshaping Online Teaching and Learning through
  LLM-driven Agents
Since the first instances of online education, where courses were uploaded to
accessible and shared online platforms, this form of scaling the dissemination
of human knowledge to reach a broader audience has sparked extensive discussion
and widespread adoption. Recognizing that personalized learning still holds
significant potential for improvement, new AI technologies have been
continuously integrated into this learning format, resulting in a variety of
educational AI applications such as educational recommendation and intelligent
tutoring. The emergence of intelligence in large language models (LLMs) has
allowed for these educational enhancements to be built upon a unified
foundational model, enabling deeper integration. In this context, we propose
MAIC (Massive AI-empowered Course), a new form of online education that
leverages LLM-driven multi-agent systems to construct an AI-augmented
classroom, balancing scalability with adaptivity. Beyond exploring the
conceptual framework and technical innovations, we conduct preliminary
experiments at Tsinghua University, one of China's leading universities.
Drawing from over 100,000 learning records of more than 500 students, we obtain
a series of valuable observations and initial analyses. This project will
continue to evolve, ultimately aiming to establish a comprehensive open
platform that supports and unifies research, technology, and applications in
exploring the possibilities of online education in the era of large model AI.
We envision this platform as a collaborative hub, bringing together educators,
researchers, and innovators to collectively explore the future of AI-driven
online education.
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Jifan Yu
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Jifan Yu
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Zheyuan Zhang
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Zheyuan Zhang
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Daniel Zhang-li
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Shangqing Tu
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Shangqing Tu
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Zhanxin Hao
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zhanxinhao
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Rui Miao Li
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Haoxuan Li
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Yuanchun Wang
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Hanming Li
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Linlu Gong
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Jie Cao
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Jie Cao
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Jiayin Lin
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2024-09-06T09:43:29.821Z
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Jiayin Lin
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Jinchang Zhou
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Fei Qin
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Haohua Wang
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Jianxiao Jiang
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Lijun Deng
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Yisi Zhan
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Chaojun Xiao
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Chaojun XIAO
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Xusheng Dai
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Xuan Yan
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Nianyi Lin
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Nan Zhang
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Ruixin Ni
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Yang Dang
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Lei Hou
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Yu Zhang
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Xu Han
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Manli Li
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Juanzi Li
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2024-09-06T09:42:07.007Z
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Juanzi Li
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Zhiyuan Liu
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2024-09-06T09:41:59.169Z
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Zhiyuan Liu
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Huiqin Liu
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Maosong Sun
From MOOC to MAIC: Reshaping Online Teaching and Learning through
  LLM-driven Agents
Since the first instances of online education, where courses were uploaded to
accessible and shared online platforms, this form of scaling the dissemination
of human knowledge to reach a broader audience has sparked extensive discussion
and widespread adoption. Recognizing that personalized learning still holds
significant potential for improvement, new AI technologies have been
continuously integrated into this learning format, resulting in a variety of
educational AI applications such as educational recommendation and intelligent
tutoring. The emergence of intelligence in large language models (LLMs) has
allowed for these educational enhancements to be built upon a unified
foundational model, enabling deeper integration. In this context, we propose
MAIC (Massive AI-empowered Course), a new form of online education that
leverages LLM-driven multi-agent systems to construct an AI-augmented
classroom, balancing scalability with adaptivity. Beyond exploring the
conceptual framework and technical innovations, we conduct preliminary
experiments at Tsinghua University, one of China's leading universities.
Drawing from over 100,000 learning records of more than 500 students, we obtain
a series of valuable observations and initial analyses. This project will
continue to evolve, ultimately aiming to establish a comprehensive open
platform that supports and unifies research, technology, and applications in
exploring the possibilities of online education in the era of large model AI.
We envision this platform as a collaborative hub, bringing together educators,
researchers, and innovators to collectively explore the future of AI-driven
online education.
MAIC, leveraging large language models and multi-agent systems, aims to improve online education through scalability and adaptivity, supported by a collaborative platform for research and innovation.
large language models LLM-driven multi-agent systems AI-augmented classroom