## 3 Research Methods
### Data Analysis  
Structural equation modeling (SEM) was employed to test the theoretical model and hypotheses, allowing for an understanding of the statistical relationships between the variables [50]. Several studies in the field of education have utilized SEM. For example, [PERSON] et al. [51] developed a structural equation model to examine the effectiveness of programming learning on the development of computational thinking. [PERSON] et al. [52] attempted to develop a model illustrating the relationships between students' perceptions of social support and their engagement in online English learning. [PERSON] et al. [53] aimed to investigate the causal relationships among these variables using SEM with a sample of prospective mathematics teachers. [PERSON] and [PERSON] [54] conducted a study with 306 learners enrolled in MOOCs in South Korea to investigate the effectiveness of programming learning on the development of computational thinking. Moreover, [PERSON] [55] conducted a longitudinal study to assess the changes in technology integration proficiency among Taiwanese teachers from grades 1 to 9. [PERSON] et al. [56] utilized structural equation modeling to uncover that the application of online learning strategies was predictive of students' online learning outcomes. [PERSON] and [PERSON] [57] investigated the correlation between participation behavior and learning, considering factors such as collective efficacy, social ability, and temporal dimensions. Therefore, this study adopted the SEM approach for several key reasons: First, the SEM method supports the estimation of latent variables in the external or measurement model and examines the interrelationships between the latent variables in the internal or structural model. Second, this approach facilitates a more comprehensive analysis of the influencing factors. Methodologically, SEM has become one of the most important statistical analysis paradigms in empirical research within the social sciences. We used SPSS 28.0 software and Amos26.0 software to conduct reliability and validity analyses and path analysis.  
The fundamental idea behind the fuzzy-set qualitative comparative analysis (fsQCA) method is organizational configurational thinking, which involves generating various configurations to express similar or different social functions and impacts [58]. For example, [PERSON] et al. [59] combined grounded theory and fsQCA to study the factors influencing the continuance intention in blended learning. In particular, fsQCA presents a holistic view by uncovering the combinations of conditions that foster the continuance intention in blended learning, granting a deeper understanding of the fundamental mechanisms and pathways involved. [PERSON] et al. (2020) revealed various mechanisms of college students' sustained engagement in online gamified learning based on the dual analysis of MPLUS and fsQCA. Additionally, research combining the PLS-SEM and fsQCA methods can be found in (2020). In general, the adoption of fsQCA in this study was motivated by several reasons: First, considering the limitations of symmetric statistical tests ([PERSON] et al., 2020; [PERSON] et al., 2020) and the existence of multiple realities, the same data set was also analyzed using fsQCA to explore the set relations of the conditions. Second, the study aimed to investigate the necessary conditions of the aforementioned variables for students' continuance intentions towards using AI tools for SDL. Third, when the outcome is equivalent, the combination of antecedent conditions is not unique. This allows for a further exploration of the configurational effects and sufficient pathways that influence students' continuance intentions. We applied fsQCA 4.1 software to analyze the configurations of the variables.