Exploratory Factor Analysis of Generative AI Usage in The Learning Process at Telkom University Surabaya
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Abstract
Purpose – The rapid development of generative artificial intelligence (generative AI) has driven its increased use in higher education, particularly to support learning activities, academic task completion, and information searching. This study aims to examine the empirical factor structure of a questionnaire on generative AI usage in the learning process, and to assess the extent to which this structure corresponds to the five technology acceptance constructs (TAM/UTAUT) used to design the instrument. The sample was drawn solely from active students at the Telkom University Surabaya Campus.
Methods – The study used a quantitative cross-sectional survey of 150 students selected through purposive sampling. A 25-item questionnaire built on five TAM/UTAUT-based constructs was administered online and analyzed using Exploratory Factor Analysis (EFA) with Principal Component Analysis (PCA) extraction and Varimax rotation. Data adequacy was tested using the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity.
Findings – The data were suitable for EFA (KMO = 0.803; Bartlett’s Test of Sphericity, p < 0.001). Five components were retained, namely Perceived Benefit, Perceived Ease, Motivation and Interest in AI Usage, Trust and Concerns toward AI, and Social Environment Influence, which together explain 65.650% of the total variance. Because these five components correspond to the five constructs used to design the instrument, the results provide initial support for the instrument’s dimensional structure rather than evidence of newly discovered factors or of factors that influence AI usage.
Research Implications – The results may inform institutional discussions on generative AI literacy that consider technical, psychological, and social aspects. The study is limited by its single institution, purposive sampling, self-reported data, cross-sectional design, and the absence of confirmatory validation. Further studies should apply CFA or SEM to an independent sample.
Keywords
- generative artificial intelligence
- exploratory factor analysis
- technology acceptance
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