Machiavellianisme dan Sikap terhadap AI Generatif pada Mahasiswa Sarjana di Indonesia

Abstract

Generative artificial intelligence (Gen AI) has become increasingly integrated into higher education, offering academic benefits while also raising concerns about academic integrity. Although previous studies have examined students’ acceptance of Gen AI, the role of dark personality traits, particularly Machiavellianism, remains underexplored in the Indonesian higher education context. This study aimed to examine the relationship between Machiavellianism and attitudes toward Gen AI among Indonesian undergraduate students. A quantitative cross-sectional correlational design was employed. The participants consisted of 226 undergraduate students from universities in Java and Sumatra who had used or were familiar with Gen AI tools such as ChatGPT, Gemini, or Claude. Participants were recruited through convenience sampling via online survey distribution. Machiavellianism was measured using the four-item Machiavellianism subscale of the Indonesian-validated Dark Triad Dirty Dozen (DTDD), while attitudes toward Gen AI were assessed using an Indonesian adaptation of the four-item AI Attitude Scale (AIAS-4). Both instruments demonstrated satisfactory internal consistency, with Cronbach’s alpha values of 0.82 for Machiavellianism and 0.86 for attitudes toward Gen AI. Data were analyzed using Pearson product–moment correlation. The results revealed a significant positive relationship between Machiavellianism and favorable attitudes toward Gen AI, r(224) = 0.264, p < 0.001, indicating that students with stronger Machiavellian tendencies tended to evaluate Gen AI more positively. Although the effect size was modest, the finding suggests that strategic, manipulative, and utilitarian personality tendencies may shape how students perceive Gen AI as an academic tool. This study contributes to the literature on personality and technology acceptance in a collectivist educational context. The findings imply the need for AI literacy programs and academic integrity policies that address not only technological competence but also ethical reflection and responsible AI use among university students.