Exploring Behavioral Patterns of Generative AI Usage in Higher Education: A Data-Driven Segmentation Approach

Nattaporn Thongsri; Yaowaphan Sontikun; Nattorn Khuntong

doi:10.3991/ijim.v20i16.62393

Authors

Nattaporn Thongsri Prince of Songkla University, Surat Thani Campus, Surat Thani, Thailand https://orcid.org/0000-0002-4077-8517
Yaowaphan Sontikun Prince of Songkla University, Surat Thani Campus, Surat Thani, Thailand https://orcid.org/0000-0001-7766-7274
Nattorn Khuntong Nakhon Si Thammarat Rajabhat University, Nakhon Si Thammarat, Thailand

DOI:

https://doi.org/10.3991/ijim.v20i16.62393

Keywords:

Generative AI, Behavioral patterns, Agricultural education, User segmentation, Clustering, AI anxiety

Abstract

This study explores behavioral patterns of generative artificial intelligence (GenAI) usage in higher education using a data-driven segmentation approach. Moving beyond intention-based models such as the technology acceptance model and the Unified Theory of Acceptance and Use of Technology, the study focuses on actual usage behavior and user heterogeneity. Data were collected through a structured questionnaire measuring AI usage behaviors and psychological factors, including trust and AI anxiety. A machine learning–based clustering approach, specifically K-means clustering, was employed to identify user segments, while ANOVA examined differences across clusters. The findings reveal three distinct groups: skeptical users, pragmatic users, and power users, with significant differences in usage frequency, trust, anxiety, and behavioral intention (p < 0.001). Notably, power users exhibit both high trust and elevated anxiety, highlighting the complex psychological dynamics of intensive AI usage.