Artificial intelligence and management education: A conceptualization of human-machine interaction

IF 5.3 2区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY ACS Applied Nano Materials Pub Date : 2024-06-10 DOI:10.1016/j.ijme.2024.101007
Stewart Clegg , Soumodip Sarker
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Abstract

The increasing use of Advanced Natural Language Processing (ANLP) models, particularly ChatGPT-4, presents opportunities and challenges to management education and research. These models can enhance the style, creativity, and analytical power of research papers, potentially shifting human scholars' roles from creators to ‘prompters’. If machines can perform educational and research tasks more effectively the role of human educators becomes a salient question in a world in which ANLP models offer clear, coherent, and polished insights, the use of which has potentially paradoxical possibilities. From one perspective, a new type of high-quality scholarship and education characterized by strong human involvement that synergistically leverages ANLP models' analytical capabilities, enabling human scholars to probe complex phenomena and make management research truly meaningful and impactful for broader audiences, is possible. We explore these questions through an ‘ideal type’ conceptualization of the possible relations between AI and management education and research.

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人工智能与管理教育:人机互动概念化
高级自然语言处理(ANLP)模型,尤其是 ChatGPT-4 的使用日益增多,为管理教育和研究带来了机遇和挑战。这些模型可以增强研究论文的风格、创造力和分析能力,从而有可能将人类学者的角色从创作者转变为 "提示者"。如果机器可以更有效地完成教育和研究任务,那么人类教育者的角色就成了一个突出的问题,因为在这个世界上,ANLP 模型可以提供清晰、连贯和精炼的见解,而这些见解的使用可能会产生矛盾。从一个角度来看,一种新型的高质量学术和教育是可能的,这种学术和教育的特点是有大量的人类参与,协同利用ANLP模型的分析能力,使人类学者能够探究复杂的现象,并使管理研究对更广泛的受众具有真正的意义和影响。我们通过对人工智能与管理教育和研究之间可能关系的 "理想类型 "概念化来探讨这些问题。
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来源期刊
CiteScore
8.30
自引率
3.40%
发文量
1601
期刊介绍: ACS Applied Nano Materials is an interdisciplinary journal publishing original research covering all aspects of engineering, chemistry, physics and biology relevant to applications of nanomaterials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important applications of nanomaterials.
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