Paper Type
Complete
Paper Number
1590
Description
This study investigates the impact of generative artificial intelligence (GenAI) on academic writing within higher education by analyzing the digital learning processes facilitated by GenAI. Through examining 626 actions of ten doctoral students engaged in GenAI-assisted academic writing, the research identifies distinct sequential behaviours and the formation of epistemic networks that differentiate high-performing students from their low-performing counterparts. Utilizing Lag Sequential Analysis (LSA) and Epistemic Network Analysis (ENA), this study contributes to both Information Systems (IS) and learning sciences research by providing insights into the effective processes of interacting with GenAI in academic writing. This study's findings suggest integrating scaffolding prompts to better engage students in their interactions with GenAI, thereby enhancing GAI-assisted academic writing. The findings aim to inform educators, students, educational technology developers, and other stakeholders in enhancing learning outcomes through the informed use of GenAI in educational settings.
Recommended Citation
Dang, Belle; Nguyen, Andy; and Hong, Yvonne, "Decoding AI-Assisted Academic Writing Process: Insights from Lag Sequential and Epistemic Network Analyses" (2024). PACIS 2024 Proceedings. 8.
https://aisel.aisnet.org/pacis2024/track14_educ/track14_educ/8
Decoding AI-Assisted Academic Writing Process: Insights from Lag Sequential and Epistemic Network Analyses
This study investigates the impact of generative artificial intelligence (GenAI) on academic writing within higher education by analyzing the digital learning processes facilitated by GenAI. Through examining 626 actions of ten doctoral students engaged in GenAI-assisted academic writing, the research identifies distinct sequential behaviours and the formation of epistemic networks that differentiate high-performing students from their low-performing counterparts. Utilizing Lag Sequential Analysis (LSA) and Epistemic Network Analysis (ENA), this study contributes to both Information Systems (IS) and learning sciences research by providing insights into the effective processes of interacting with GenAI in academic writing. This study's findings suggest integrating scaffolding prompts to better engage students in their interactions with GenAI, thereby enhancing GAI-assisted academic writing. The findings aim to inform educators, students, educational technology developers, and other stakeholders in enhancing learning outcomes through the informed use of GenAI in educational settings.
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Education