Determinants of Honest Academic Behavior Among Indonesian NonEngineering Students in the Era of AI: A TPB-Based Structural Equation Modeling Study - B.M.A.S. Anaconda Bangkara (Pelopor Publikasi Akademika, 2026)
The rapid integration of artificial intelligence (AI) tools has reshaped learning practices across non-engineering disciplines, including the social sciences, business, humanities, education, and law. While these technologies offer clear pedagogical benefits, they also raise growing concerns regarding academic integrity, particularly in writing-intensive fields. This study examines the psychological factors that promote honest academic behavior among Indonesian non-engineering students, drawing on the Theory of Planned Behavior (TPB) as its theoretical foundation. A total of 375 undergraduate students from universities across Indonesia completed a structured online survey comprising 35 Likert-scale items measuring attitude toward honest behavior (ATB), subjective norms (SN), perceived behavioral control (PBC), behavioral intention (BI), and actual honest behavior (AB). Data were analyzed using Structural Equation Modeling (SEM) with AMOS to evaluate construct reliability, validity, and hypothesized relationships. The results demonstrate that ATB, SN, and PBC significantly influence BI, collectively accounting for 52% of its variance. Behavioral intention emerged as the strongest predictor of actual honest behavior (? = 0.61, p < .001), while perceived behavioral control also showed a modest but significant direct effect (? = 0.12, p = .028), together explaining 48% of the variance in AB. Bootstrapping analysis further confirmed the mediating role of behavioral intention in all antecedent–behavior pathways. These findings extend the application of the Theory of Planned Behavior to AI-enhanced learning environments in non-engineering disciplines and underscore the role of ethical attitudes, social influence, and perceived competence in maintaining academic integrity. The study provides practical insights for higher education institutions in developing responsible AI-use policies tailored to non-engineering students.