The Intrinsic Requirements and Implementation Strategies ofInterdisciplinary Education in Universitiesin the Era of Artificial Intelligence

Wang Songdi; Zhang Wenjun

PDF(1823 KB)

Education and Teaching Research ›› 2026, Vol. 40 ›› Issue (7) : 1-12.

Wang Songdi, Zhang Wenjun

Author information +

History +

Abstract

The rapid advancement of AI and its displacement of highly educated individuals in the workforce have posed significant challenges to talent cultivation in universities and new opportunities for higher education reform.Determining what kind of talents universities should cultivate and how to effectively nurture them has become an urgent matter.Interdisciplinarity is a crucial characteristic in knowledge production during the era of AI,thus cultivating new interdisciplinary talents has emerged as a fresh requirement for talent cultivation.Universities should leverage AI technology to accelerate educationalinnovation,break down the barriers of traditional disciplines,and actively explore new directions for ″AI+″ discipline development.Efforts should focus on enhancing the digital level of governance,empowering the development of interdisciplinary teaching teams,and constructing a new interdisciplinary education model involving collaboration among industry,academia,and research.The ultimate aim is to cultivate innovative talents equipped with the abilities to integrate knowledge from multiple disciplines,communicate and collaborate across disciplinary boundaries,critically reflect on different fields of study,and engage in comprehensive interdisciplinary practice.These measures will propel the independentcultivation capacity and quality of innovative talent in universities to a newlevel.

Key words

artificial intelligence / interdisciplinary / higher education / talent cultivation / knowledge production models

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Wang Songdi, Zhang Wenjun. The Intrinsic Requirements and Implementation Strategies ofInterdisciplinary Education in Universitiesin the Era of Artificial Intelligence[J]. Education and Teaching Research. 2026, 40(7): 1-12

References

[1] 杨宗凯,王俊,吴砥,等.ChatGPT/生成式人工智能对教育的影响探析及应对策略[J].华东师范大学学报(教育科学版),2023,41(7):26-35.
[2] Open AI.GPTs are GPTs:An early look at the labor market impact potential of large language models[EB/OL].(2023-03-17)[2024-12-21].https://openai.com/research/gpts-are-gpts.
[3] 王彦雷.人工智能与高等教育发展范式转型研究[J].高等理科教育,2020(3):73-78.
[4] 肖峰.生成式人工智能与知识生产新形态:关于三阶知识生产的探析[J].学术研究,2023(10):50-57,2,177.
[5] 克拉克.高等教育系统:学术组织的跨国研究[M].王承绪,徐辉,殷企平,等,译.杭州:杭州大学出版社,1994:11.
[6] 陈洪捷.知识生产模式的转变与博士质量的危机[J].高等教育研究,2010,31(1):57-63.
[7] 吉本斯,利摩曰,诺沃提尼,等.知识生产的新模式:当代社会科学与研究的动力学[M].陈洪捷,译.北京:北京大学出版社,2011:4-9.
[8] 白强.基于知识生产模式Ⅲ的高校学科高质量发展研究[J].科学管理研究,2023,41(4):41-47.
[9] 陈晓珊,戚万学.知识机器生产模式与教育新隐喻[J].教育研究,2023,44(10):33-43.
[10] 李冀红,庄榕霞,年智英,等.面向人机协同的创新能力培养:兼论面向智能时代的创造性人才诉求[J].中国电化教育,2021(7):36-42,61.
[11] 李忠,李致远.不确定性视域中的高等教育变革:视域、知识观、学习观与教学观[J].中国电化教育,2022(9):61-68.
[12] 孙典,王莉,商立媛.人工智能赋能我国高等教育高质量发展的内涵、困境及路径[J].现代教育管理,2024(6):34-42.
[13] Chang Y,Wang X,Wang J,et al.A survey on evaluation of large language models[EB/OL].(2023-08-28)[2024-09-21].https://arxiv.org/pdf/2307.03109.pdf.
[14] 刘邦奇,聂小林,王士进,等.生成式人工智能与未来教育形态重塑:技术框架、能力特征及应用趋势[J].电化教育研究,2024,45(1):13-20.
[15] 黄翰.人工智能终将替代人类?[EB/OL].(2023-04-16)[2024-01-05].https://www.gdtv.cn/tv/ba9ce7a5991d187139601a4080fc5cae.
[16] 张绒.生成式人工智能技术对教育领域的影响:关于ChatGPT的专访[J].电化教育研究,2023,44(2):5-14.
[17] 杨晓哲,王晴晴,王若昕.生成式人工智能的有限能力与教育变革[J].全球教育展望,2023,52(6):3-12.
[18] Gebbie K M,Mason Meier B,Bakken S,et al.Training for interdisciplinary health research defining the required competencies[J].Journal of AlliedHealth,2008,37(2):65-70.
[19] Borrego M,Cutler S.Constructive alignment of interdisciplinary graduate curriculum in engineering and science:an analysis of successful IGERT proposals[J].Journal of Engineering Education,2010,99(4):355-369.
[20] Lattuca L R,Knight D,Bergom I.Developing a measure of interdisciplinary competence[J].The International Journal of Engineering Education,2013,29(3):726-739.
[21] 徐岚,陶涛,周笑南.跨学科研究生核心能力及其培养途径:基于美国IGERT项目的分析[J].学位与研究生教育,2018(5):61-68.
[22] 雷普克.如何进行跨学科研究[M].傅存良,译.北京:北京大学出版社,2016:4.
[23] 吴虑,杨磊.ChatGPT赋能学习何以可能[J].电化教育研究,2023,44(12):28-34.
[24] 刘永,胡钦晓.论人工智能教育的未来发展:基于学科建设的视角[J].中国电化教育,2020(2):37-42.
[25] 教育部国家发展改革委财政部印发《关于“双一流”建设高校促进学科融合加快人工智能领域研究生培养的若干意见》的通知[EB/OL].(2020-01-21)[2025-01-05].http://www.moe.gov.cn/srcsite/A22/moe_-826/202003/t20200303_-426801.html.
[26] 教育部关于公布2022年度普通高等学校本科专业备案和审批结果的通知[EB/OL].(2023-04-06)[2024-01-05].http://www.moe.gov.cn/srcsite/A08/moe-1034/s4930/202304/t20230419-1056224.html?eqid=ab6c2edb002370770000000264638ac3.
[27] 熊华军.MIT跨学科博士生的培养及其启示[J].比较教育研究,2006(4):46-49.
[28] 茹宁,李薪茹.突破院系单位制:大学“外延型”跨学科组织发展策略探究[J].中国高教研究,2018(11):71-77.
[29] 吴南中,陈咸彰,冯永.从“失序”到“有序”:生成式人工智能教育应用的转向及其生成机制[J].远程教育杂志,2023,41(6):42-51.
[30] 加涅,韦杰,戈勒斯,等.教学设计原理[M].王小明,庞维国,陈保华,等,译.上海:华东师范大学出版社,1999:4.
[31] 梁云真,刘瑞星,高思圆.中小学“人工智能+X”跨学科融合教学:理论框架与实践策略[J].电化教育研究,2022,43(10):94-101.
[32] 赵渊.人工智能时代的高等教育抉择:方向、策略与路径[J].社会科学战线,2019(10):234-241.