Analyzing Students' Information Behavior in Generative AI-Supported Small Group Discussions
Xiuyu Chen, Shihui Feng · 2024
Generative artificial intelligence (AI) tools utilize machine learning models to create new content in response to human-provided prompts, which can automate the creation of large amounts of content in a short time. This study analyzed students' information behavior in small group discussions where the students were encouraged to use generative AI tools. Descriptive and lag sequential analysis methods were employed to examine the characteristics and patterns of students' information behavior according to Ellis' model of information seeking.The results indicated that although students frequently used generative AI tools as primary information sources, they also sought additional resources to satisfy their informational needs. Additionally, students sometimes copied and pasted useful information from generative AI tools into group documents to share with their group members. Lag sequential analysis revealed that students typically began their information seeking process with generative AI tools, followed by exploring additional information sources.