Supporting data for “Uncovering the Dynamics of AI Use in Academic Writing”

This dataset was collected as part of a doctoral research dissertation exploring how generative AI tools support students’ academic writing. The study aimed to examine the profiles of AI use among university students and how variables such as AI self-efficacy, writing self-efficacy, and self-regulated learning predict these profiles and their associated learning outcomes.

The data comprises survey responses from 1073 students, who completed a questionnaire measuring constructs such as writing self-efficacy (WSE), AI self-efficacy (ASE), self-regulated learning (SRL), academic grit (AG), writing motivation (WM), perceived improvement (PI), and critical thinking (CT), among others. All responses are anonymized. The dataset also includes aggregated composite variables and calculated profile membership for subsequent quantitative analysis.