從慕課到智課：透過大型語言模型驅動的智能體重塑線上教學 （注：MAIC作為新創術語，此處意譯為"智課"，既保留與MOOC的音韻對應，又體現LLM驅動的智能化特徵。智能體是Agent在AI領域的標準譯法。）

From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents

September 5, 2024

作者: Jifan Yu, Zheyuan Zhang, Daniel Zhang-li, Shangqing Tu, Zhanxin Hao, Rui Miao Li, Haoxuan Li, Yuanchun Wang, Hanming Li, Linlu Gong, Jie Cao, Jiayin Lin, Jinchang Zhou, Fei Qin, Haohua Wang, Jianxiao Jiang, Lijun Deng, Yisi Zhan, Chaojun Xiao, Xusheng Dai, Xuan Yan, Nianyi Lin, Nan Zhang, Ruixin Ni, Yang Dang, Lei Hou, Yu Zhang, Xu Han, Manli Li, Juanzi Li, Zhiyuan Liu, Huiqin Liu, Maosong Sun

cs.AI

摘要

自線上教育首次將課程上傳至可共享的網絡平台以來，這種拓展人類知識傳播規模以觸達更廣泛受眾的模式，已引發廣泛討論並獲得普遍應用。鑑於個性化學習仍具巨大改進潛力，新興人工智慧技術持續融入此學習形式，催生了教育推薦系統、智慧導學等多類教育AI應用。大型語言模型所展現的智慧特性，使這些教育增強功能得以建構在統一的基礎模型上，實現更深層次的整合。在此背景下，我們提出MAIC（大規模AI賦能課程），這種新型線上教育形式利用LLM驅動的多智能體系統構建AI增強課堂，在可擴展性與自適應性間取得平衡。除探討概念框架與技術創新外，我們還在中國頂尖學府清華大學開展初步實驗。基於500餘名學生的十萬餘條學習記錄，我們獲取了一系列有價值的觀測數據與初步分析。該項目將持續演進，最終目標是建立一個支持並統籌研究、技術與應用的綜合開放平台，探索大模型AI時代線上教育的可能性。我們設想該平台成為協作樞紐，匯聚教育者、研究人員與創新者，共同探索AI驅動的線上教育未來。

English

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.