Situated usage of generative AI in policy education: implications for teaching, learning, and research

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.