Situated usage of generative AI in policy education

Implications for Teaching, Learning, and Research

Bibliographic Data

ID6433807
AuthorsGleb Papyshev (0000-0002-4582-4973, Hong Kong University of Science and Technology, corresponding author)
Year2024
Volume18
Issue2
Pages311-328
Publication date2024-06-21
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Asian Public Policy (JOURNAL)
Journal identifiersISSN: 1751-6242 • E-ISSN: 1751-6234
PublisherTaylor & Francis (PUBLISHER • GB)
DOI10.1080/17516234.2024.2370716
OpenAlexW4399906405
LanguageEN
Citations received5
References cited30

This study explores the contrasting sentiments towards the use of generative AI technologies among research postgraduate students in public policy. 14 interviews about the usage of generative AI technologies in the students’ research, teaching, and learning practices were conducted and used as the empirical data source for this project. Through qualitative and sentiment analysis, the research identified domains where students applied generative AI and discovered both positive and negative sentiments within the same application domains. The divergence in sentiments was interpreted using the ‘plans and situated actions’ framework, suggesting that technological expectations constrained by contextual environments lead to varied experiences of ‘enchantment’ and ‘disenchantment’. The findings emphasize the imperative for adaptable academic policies delineating acceptable AI usage in research, the implementation of discipline-specific AI training in universities, and the development of discipline-specific AI systems to cater to unique academic field needs

Generative grammar · Mathematics education · Pedagogy · Situated · Situated learning · Sociology · Computational and Text Analysis Methods · Computer Science · Ethics and Social Impacts of AI · Psychology · Qualitative Comparative Analysis Research · Artificial Intelligence

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