## 5 Discussion  
This study aimed to explore the relationship between prompting techniques written in Spanish and the responses generated by a large language model (LLMs), specifically ChatGPT (free version 3.5), as employed by higher education students. Additionally, the study examined how these techniques clustered into emergent strategies and assessed their overall impact on ChatGPT's output quality. To achieve this purpose, the present research aimed to fulfill the following three key objectives: (i) to identify the emerging prompt strategies employed by higher education students; (ii) to examine the relationship between the types of techniques used \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Item Statistics**} & \multicolumn{4}{c}{**Q3 Correlation Matrix**} \\ \hline  & **Proportion** & **Measure** & **S.E. Measure** & **Infit** & **Outfit** & **CT2** & **CT1** & **RBT21** \\ \hline CT2 & 0.478 & 0.184 & 0.355 & 0.771 & 0.624 & CT2 & — & \\ CT1 & 0.594 & \(-\)0.83 & 0.358 & 0.81 & 0.582 & CT1 & 0.004 & — & \\ RBT21 & 0.319 & 1.615 & 0.37 & 0.905 & 0.809 & RBT21 & \(-\)0.277 & \(-\)0.274 & — \\ \hline \hline \end{tabular} Note. Infit= Information-weighted mean square statistic; Outfit = Outlier-sensitive means square statistic.  
\end{table} 
Table 8: Item statistics—dichotomous model for contextual strategies and Q3 correlation matrix. to formulate prompts and the quality of the responses generated by the large language model (LLM); and (iii) to describe both simple and complex strategies observed in students' prompt-writing processes. From the literature review, we preliminarily proposed two categories of techniques: circumstantial and request-based. Circumstantial techniques were defined as those that provide context and structure to the prompt, forming a foundational layer that guides the interaction. Conversely, request-based techniques refer to those explicitly designed to articulate the query or task directed to the LLMs. For this study, 10 circumstantial techniques and 11 request-based techniques were identified and defined (see Tables 1 and 2). This categorization served as a practical synthesis of existing research, offering a structured framework that was applied to analyze the students' prompt-writing practice. The key contribution of this study lies in the evidence it provides regarding the impact of prompt engineering strategies on the quality of responses generated by ChatGPT in Spanish. Two emergent strategies were identified, each clustering distinct prompt engineering techniques systematically identified in this research: (i) Guide Contextualization Strategy (GCS) and (ii) Specific Purpose Strategy (SPS). The GCS clusters techniques that focus on providing an informative framework to structure the language model's response generation, ensuring that the AI operates within a well-defined context. In contrast, the SPS group's techniques aimed at directing the model's output by explicitly stating the underlying purpose of the request, thereby shaping the AI's response toward a specific goal. The characteristics and distinctions of each strategy are detailed in the following sections. Three techniques were clustered into the group of contextual strategies (GCS). The linear regression analysis revealed that CT1 (Context), CT2 (Ambiguity Reduction), and RBT21 (Request with Context) had a significant effect on the quality of responses generated by the model, F (1, 67) = 17.7, \(p\) < 0.001, R\({}^{2}\) = 0.209, \(\beta\) = 0.457. These findings indicate that incorporating contextual information into prompts enhances the accuracy and relevance of the generated outputs. Although the R\({}^{2}\) value of 0.209 may appear modest, it aligns with the medium effect size threshold proposed by [PERSON] [31], who defined values of 0.02, 0.15, and 0.35 as small, medium, and large, respectively. In the context of educational and behavioral research, such effect sizes are considered meaningful, particularly when dealing with complex phenomena influenced by multiple factors.