## 4 Results  
### Emerging Dimensions of Prompt Strategies  
To address Objective 1, a tetrachoric correlation analysis was conducted using the statistical software Jamovi (version 2.6.44) to examine the relationships between the dichotomous variables indicating the presence or absence of each technique. Items 3, 6, 7, 10, and 16 were excluded from the analysis due to insufficient response variability. Notably, negative correlations were identified for items 11, 12, 14, and 20, as indicated by the red lines in the network diagram presented in Figure 1.  
A moderate negative tetrachoric correlation (rt = -0.518) was observed between the use of the direct request technique and the mixed or multi-task request technique. This indicates that as the presence of one of these strategies increases, the presence of the other tends to decrease. In other words, students who frequently used direct prompts tended not to employ complex or multi-tasking formulations simultaneously, suggesting a possible distinction in their approach to interacting with the model.  
High positive tetrachoric correlations (>0.7, in Table 3) were identified between indicators 1 and 2, indicators 1 and 21, indicators 2 and 21, and indicators 13 and 21. Conversely, indicator 14 exhibited a strong negative correlation with other techniques.  
The RBT14 shows a negative correlation with the other techniques. This inverse association indicates that when an open-ended question is formulated (e.g., \"What does it imply...?\"), it is less likely that the prompt will include elements intended to frame, constrain, or structure the response. In other words, the use of open-ended questions tends to be accompanied by fewer contextual or structural cues, suggesting a preference for unstructured interaction styles when employing exploratory formulations.  
Figure 2 illustrates the process preceding the exploratory factor analysis (EFA), where 69 relevant responses were selected from the 102 participants. Different models were tested: first, considering only positively correlated items, and then including both positive and negative correlations. The EFA was conducted to determine the most suitable model for grouping the techniques (see Figure 2). Model 5 demonstrated a good fit, with an RMSEA of 0.00 (90% CI: [0.00, 0.018]), an excellent TLI of 0.977, a chi-square (\(\chi^{2}\)) value of 8.76 (df = 9, \(p\) = 0.460), and an explained variance of 45.2%. Meanwhile, Model 3 exhibited the best overall fit, with an RMSEA of 0.00 (90% CI: [0.00, 0.019]), a \(\chi^{2}\) value of 6.05 (df = 1, \(p\) = 0.83), and an explained variance of 53.6%. The KMO values were 0.819 for Model 5 and 0.741 for Model 3, indicating strong sampling adequacy. Between the two best-fitting models, Model 3 was ultimately selected due to its stronger theoretical alignment and better conceptual coherence regarding the two emerging factors.  
Figure 1: Network structure. The thickness of the lines represents the strength of the correlation between two factors. Green and red lines indicate positive and negative correlations, respectively, among the 16 items (see numbering in Tables 1 and 2). Note: CT: Circumstantial Technique; RBT: Request-Based Technique.  
Model 3 revealed two theoretical strategies: the contextual strategy, comprising techniques 1, 2, and 21, and the purpose-related strategy, comprising techniques 5 and 19 (see Table 4).