K-Means Clustering for Segmenting AI Survey Respondents: Analysis of Information Sources and Impact Perceptions
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Abstract
This study employs K-Means clustering to analyze survey data from 91 university students, aiming to segment respondents based on their information-seeking behaviors (Question 2) and impact perceptions (Question 3) of artificial intelligence (AI). Two distinct clusters emerged: “Optimistic Problem Solvers,” who favor formal channels such as scholarly websites, peer-reviewed papers, and guided discussions, and express strong confidence in AI’s problem-solving capabilities with low concern for job displacement or dehumanization; and “Critical Watchers,” who rely more on informal, rapidly updated media (e.g., social platforms, general web searches) and exhibit heightened apprehension regarding AI’s socio-economic and ethical risks. Demographically, the former group skews toward sophomores with consistent GPAs and quantitatively oriented majors, while the latter displays broader disciplinary representation, balanced gender composition, and greater academic variability. These findings validate a dual-dimensional segmentation framework that integrates source behavior with perceptual orientation, highlighting the inadequacy of one-size-fits-all AI education. The study recommends differentiated instructional strategies, deep-dive, research-oriented modules for problem-solvers and trust-building, narrative-driven outreach for watchers, and outlines future research directions including larger, multi-institutional samples, longitudinal tracking, and mixed-methods approaches to refine and validate these profiles.
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