Empowering Education with LLMs - The Next-Gen Interface and Content Generation

被引:16
|
作者
Moore, Steven [1 ]
Tong, Richard [2 ]
Singh, Anjali [3 ]
Liu, Zitao [4 ]
Hu, Xiangen [5 ]
Lu, Yu [6 ]
Liang, Joleen [7 ]
Cao, Chen [8 ]
Khosravi, Hassan [9 ]
Denny, Paul [10 ]
Brooks, Chris [3 ]
Stamper, John [1 ]
机构
[1] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
[2] Carnegie Learning, Pittsburgh, PA USA
[3] Univ Michigan, Ann Arbor, MI 48109 USA
[4] Jinan Univ, Guangzhou 510632, Guangdong, Peoples R China
[5] Univ Memphis, Memphis, TN 38152 USA
[6] Beijing Normal Univ, Beijing 100875, Peoples R China
[7] Squirrel AI Learning, Shanghai 200030, Peoples R China
[8] Univ Sheffield, Sheffield, England
[9] Univ Queensland, St Lucia, Qld 4072, Australia
[10] Univ Auckland, Auckland 1010, New Zealand
关键词
Large language models; Educational content creation; human-AI partnerships; learnersourcing; robosourcing;
D O I
10.1007/978-3-031-36336-8_4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We propose the first annual workshop on Empowering Education with LLMs - the Next-Gen Interface and Content Generation. This full-day workshop explores ample opportunities in leveraging humans, AI, and learning analytics to generate content, particularly appealing to instructors, researchers, learning engineers, and many other roles. The process of humans and AI cocreating educational content involves many stakeholders (students, instructors, researchers, instructional designers, etc.), thus multiple viewpoints can help to inform what future generated content might be useful, new and better ways to assess the quality of such content and to spark potential collaborative efforts between attendees. Ultimately, we want to illustrate how everyone can leverage recent advancements in AI, making use of the latestmachine learning methods and large language models (LLMs), and engage all participants in shaping the landscape of challenges and opportunities in this space. We wish to attract attendees interested in scaling the generation of instructional and assessment content and those interested in the use of online learning platforms.
引用
收藏
页码:32 / 37
页数:6
相关论文
共 50 条
  • [1] Workshop on Empowering Education with LLMs - the Next-Gen Interface and Content Generation
    Moore, Steven
    Tong, Richard
    Singh, Anjali
    Liu, Zitao
    Hu, Xiangen
    Lu, Yu
    Liang, Joleen
    Cao, Chen
    Khosravi, Hassan
    Denny, Paul
    Brooks, Chris
    Stamper, John
    CEUR Workshop Proceedings, 2023, 3487
  • [2] THE NEXT NEXT-GEN
    Marcos Molano, Maria del Mar
    Santorum Gonzalez, Michael
    REVISTA ICONO 14-REVISTA CIENTIFICA DE COMUNICACION Y TECNOLOGIAS, 2009, 7 (01): : 132 - 139
  • [3] NEXT-GEN
    Kosowatz, John
    MECHANICAL ENGINEERING, 2019, 141 (03) : 10 - 11
  • [4] Next-Gen Sensor Fusion for Next-Gen Sensors and Driving Functions
    Richter E.
    VDI Berichte, 2022, 2022 (2405): : 37 - 58
  • [5] Next-gen immunohistochemistry
    Rimm, David L.
    NATURE METHODS, 2014, 11 (07) : 773 - 773
  • [6] Next-gen immunohistochemistry
    David L Rimm
    Nature Methods, 2014, 11 : 381 - 383
  • [7] Next-Gen Retirement
    Vough, Heather C.
    Bataille, Christine D.
    Sargent, Leisa
    Lee, Mary Dean
    HARVARD BUSINESS REVIEW, 2016, 94 (06) : 104 - 107
  • [8] Next-Gen Bioprocessing
    Sterling, Johe
    Genetic Engineering and Biotechnology News, 2019, 39 (S4):
  • [9] Next-gen scope
    不详
    MICRO, 2005, 23 (07): : 14 - 14
  • [10] Next-Gen researcher
    Jernigan, Rebecca C.
    PHOTONICS SPECTRA, 2009, 43 (03) : 28 - 28