Journal of Vocational, Informatics and Computer Education
Vol 3, No 2 (2025): December 2025

Student Resistance to ChatGPT in Indonesia: Extended IRT with PLS-SEM Analysis

Andi Muhammad Faiz Iqbal (Universitas Negeri Makassar)
Nurul Hasmi (Universitas Negeri Makassar)
Devi Miftahul Jannah (Universitas Negeri Makassar)
Rizki Wahyu Hunian Putra (Universitas Islam Negeri Raden Intan Lampung)



Article Info

Publish Date
15 Dec 2025

Abstract

The integration of Artificial Intelligence (AI) in higher education is growing, including the use of ChatGPT as a tool to assist students academically by improving access to information and promoting independent learning. Nonetheless, some students have shown reluctance due to worries about its reliability, academic morals, and changes in conventional learning principles. This research intends to explore how various barriers, such as usage barrier, value barrier, risk barrier, tradition barrier, image barrier, perceived cost barrier, and ethical considerations, contribute to student hesitance regarding ChatGPT. A quantitative method was utilized through Partial Least Squares Structural Equation Modeling (PLS-SEM), gathering data from an online survey of 77 students from Universitas Negeri Makassar. Findings reveal that only the risk barrier (β = 0. 417; p = 0. 006) and the tradition barrier (β = −0. 400; p = 0. 029) have a significant impact on resistance, with the risk barrier being the most influential, while the other factors showed no notable effects. These results suggest that psychological and cultural factors are more significant than practical obstacles in influencing resistance to generative AI and broaden the Innovation Resistance Theory (IRT) by factoring in ethical issues. The study advises creating teaching strategies that find a balance between using technology and maintaining academic honesty, while also promoting further research through multigroup and longitudinal methods.

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Journal Info

Abbrev

VOICE

Publisher

Subject

Computer Science & IT Education

Description

1. Informatics and Computing Research addressing the design, development, implementation, and evaluation of computing technologies relevant to educational, professional, and digital learning environments, including but not limited to: Artificial Intelligence and Machine Learning Deep Learning and ...