@article{Yu2026, 
author = {Ji-Fan Yu and Daniel Zhang-Li and Zhe-Yuan Zhang and Yu-Cheng Wang and Hao-Xuan Li and Joy Jia Yin Lim and Zhan-Xin Hao and Shang-Qing Tu and Lu Zhang and Xu-Sheng Dai and Jian-Xiao Jiang and Shen Yang and Fei Qin and Ze-Kun Li and Bing-Lin Liu and Xin Cong and Bin Xu and Lei Hou and Man-Li Li and Juan-Zi Li and Hui-Qin Liu and Yu Zhang and Zhi-Yuan Liu and Mao-Song Sun},
title = {From MOOC to MAIC: Reimagine Online Teaching and Learning Through LLM-Driven Agents},
year = {2026},
journal = {Journal of Computer Science and Technology},
volume = {41},
number = {1},
pages = {394-414},
keywords = {intelligent tutoring system, large language model (LLM), multi-agent system, online education},
url = {https://www.sciopen.com/article/10.1007/s11390-025-6000-0},
doi = {10.1007/s11390-025-6000-0},
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 100000 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.}
}