Learning Motivation Mediates Growth Mindset, Self-Efficacy, and AI Usefulness Impact on Programming Problem-Solving Skills - Fardan, Muhammad; Fathahillah, Fathahillah; Fakhri, M. Miftach; Sanatang, Sanatang; Adiba, Fhatiah; Soeharto, Soeharto; Amukune, Stephen (Universitas Islam Negeri Raden Intan Lampung, 2025)
Artificial Intelligence has rapidly developed, especially in education and programming, providing advantages in improving learning efficiency and personalizing educational content. This study examines the relationships between learning motivation and problem-solving skills, as well as factors influencing learning motivation, namely growth mindset, self-efficacy, and perceived usefulness of Artificial Intelligence. Data collected from 276 students were analyzed using Partial Least Squares Structural Equation Modeling. The results show that growth mindset, self-efficacy, and perceived usefulness significantly influence learning motivation. Additionally, learning motivation strongly predicts problem-solving skills in programming tasks. These findings emphasize the critical role of psychological factors in fostering learning motivation and improving problem-solving abilities within Artificial Intelligence-enhanced programming environments. This research offers valuable insights for educators and instructional designers to develop effective strategies that integrate psychological support and Artificial Intelligence tools, ultimately enhancing student learning outcomes.