Growth Mindset and AI Generative Dependency among Students: The Role of Achievement Goal Orientations - Fawwaz Adzansyah Islamy; Fiki Febriani; Puspita Sari; Herdian Herdian (STAI Hubbulwathan Duri, 2026)
The increasing integration of generative artificial intelligence (GenAI) in education has raised concerns regarding students’ potential dependency on these technologies. This study examines the relationships between growth mindset, achievement goal orientations, and generative AI dependency among secondary school students in Indonesia. A total of 191 students (junior high school = 180; senior high school = 11) were selected using purposive sampling based on prior experience using GenAI for academic purposes. Using a quantitative cross-sectional survey and Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS 4, generative AI dependency was operationalized as psychological dependency characterized by cognitive preoccupation, negative consequences, and withdrawal. The results indicate that growth mindset significantly predicts mastery goal orientation (β = 0.304, p 0.001) and performance goal orientation (β = 0.284, p 0.001), but does not directly predict generative AI dependency (β = 0.055, p = 0.599). In addition, mastery goal orientation (β = −0.213, p = 0.115) and performance goal orientation (β = 0.110, p = 0.469) do not significantly predict generative AI dependency. The structural model explains a limited proportion of variance in generative AI dependency (R² = 0.032). These findings suggest that motivational constructs such as growth mindset and achievement goal orientations are associated with students’ learning orientations but are insufficient to explain dependency-related engagement with generative AI. The study highlights the need for future research to incorporate regulatory and epistemic factors to better understand students’ dependency on GenAI in educational contexts.