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From MOOC to MAIC: Reimagine Online Teaching and Learning Through LLM-Driven Agents

  • Ji Fan Yu
  • , Daniel Zhang-Li
  • , Zhe Yuan Zhang
  • , Yu Cheng Wang
  • , Hao Xuan Li
  • , Joy Jia Yin Lim
  • , Zhan Xin Hao
  • , Shang Qing Tu
  • , Lu Zhang
  • , Xu Sheng Dai
  • , Jian Xiao Jiang
  • , Shen Yang
  • , Fei Qin
  • , Ze Kun Li
  • , Bing Lin Liu
  • , Xin Cong
  • , Bin Xu
  • , Lei Hou
  • , Man Li Li
  • , Juan Zi Li
  • Hui Qin Liu, Yu Zhang, Zhi Yuan Liu, Mao Song Sun
  • Tsinghua University

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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. Since personalized learning still holds significant potential for improvement, new artificial intelligence (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 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, Beijing, one of the leading universities in China. Drawing from more than 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 to explore the possibilities of online education in the era of large-model AI. We envision this platform as a collaborative hub that brings together educators, researchers, and innovators to collectively explore the future of AI-driven online education.

Original languageEnglish
Pages (from-to)394-414
Number of pages21
JournalJournal of Computer Science and Technology
Volume41
Issue number1
DOIs
StatePublished - Jan 2026
Externally publishedYes

Keywords

  • intelligent tutoring system
  • large language model (LLM)
  • multi-agent system
  • online education

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