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

Clegg, S; Sarker, S

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

Clegg, S

Sarker, S

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: International Journal of Management Education, 2024, 22, (3)
Issue Date:: 2024-11-01

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Field	Value	Language
dc.contributor.author	Clegg, S https://orcid.org/0000-0001-6083-4283
dc.contributor.author	Sarker, S
dc.date.accessioned	2025-02-28T00:26:58Z
dc.date.available	2025-02-28T00:26:58Z
dc.date.issued	2024-11-01
dc.identifier.citation	International Journal of Management Education, 2024, 22, (3)
dc.identifier.issn	1472-8117
dc.identifier.issn	2352-3565
dc.identifier.uri	http://hdl.handle.net/10453/185446
dc.description.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.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	International Journal of Management Education
dc.relation.isbasedon	10.1016/j.ijme.2024.101007
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1302 Curriculum and Pedagogy, 1503 Business and Management, 1505 Marketing
dc.subject.classification	3507 Strategy, management and organisational behaviour
dc.subject.classification	3901 Curriculum and pedagogy
dc.title	Artificial intelligence and management education: A conceptualization of human-machine interaction
dc.type	Journal Article
utslib.citation.volume	22
utslib.for	1302 Curriculum and Pedagogy
utslib.for	1503 Business and Management
utslib.for	1505 Marketing
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Business
pubs.organisational-group	University of Technology Sydney/Faculty of Business/Management Discipline
utslib.copyright.status	closed_access	*
dc.date.updated	2025-02-28T00:26:57Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	22
utslib.citation.issue	3

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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http://hdl.handle.net/10453/185446