Abstract
<title>Abstract</title> <p>This study aims to analyze the impact of ChatGPT usage, learning content, and digital competence on students' learning motivation. A total of 366 respondents were obtained through stratified random sampling to ensure proportional representation across different student groups. Data were collected using a structured questionnaire measured on a Likert scale, and the study employs a Structural Equation Modeling (SEM) approach using Smart PLS to evaluate both the measurement model (validity and reliability) and the structural relationships among variables, including moderating effects. The results indicate that ChatGPT usage has a significant positive effect on students' learning motivation, confirming its role as an innovative learning support tool. However, its impact on digital competence is not statistically strong, suggesting that mere exposure to ChatGPT does not automatically enhance students’ digital skills without guided use. Additionally, learning content has a significant and positive influence on both digital competence and learning motivation, indicating that relevant, interactive, and well-structured content is a critical driver of both skill development and student engagement. The analysis of moderating effects reveals that the interaction between ChatGPT usage and learning motivation is statistically significant, highlighting that the effectiveness of ChatGPT depends on how it is utilized within the learning process. Furthermore, digital competence significantly moderates the relationship between learning content and learning motivation, implying that students with higher digital competence are more capable of maximizing the benefits of high-quality learning content, thereby increasing their motivation. These findings highlight the important role of ChatGPT and well-designed learning content in enhancing students' learning motivation. Moreover, digital competence serves as a key moderating factor that strengthens the relationship between learning content and motivation. The study also confirms the robustness of the model through satisfactory goodness-of-fit indicators and substantial explanatory power, indicating that the proposed variables meaningfully explain variations in learning motivation.</p>