## 5 Discussion
Therefore, the conclusion that incorporating contextual information into prompts enhances the accuracy and relevance of the generated outputs remains valid, as it is supported by a statistically significant and practically relevant association. In addition, the analysis of tetrachoric correlations revealed a strong association between CT1 (Context) and CT2 (Ambiguity Reduction), rt = 0.933, suggesting that these techniques are frequently employed together. CT1 also demonstrated positive correlations with CT5 (Goal or Purpose Indicator), rt = 0.680, and RBT21 (Request with Context), rt = 0.801, indicating that students who apply contextual strategies tend to incorporate elements that clarify the purpose of their prompts. In contrast, RBT14 (Open-Ended Question Request) showed strong negative correlations with CT1, rt = -0.842, and CT2, rt = -0.866, suggesting that this technique may reduce the likelihood of using clearly contextualized prompting strategies. A possible interpretation is that, in some cases, when students formulate entirely open-ended questions without anchoring them to specific topics or goals, they may overlook key contextual elements needed to guide the generative model effectively. This could result in prompts that lack precision or relevance, potentially affecting the quality and specificity of the AI-generated responses. These findings underscore the importance of fostering a balance between openness and contextual framing in the development of prompt engineering skills. These findings are consistent with previous research that has identified the use of contextual information as a crucial factor in generating more accurate responses from LLMs. For example, [PERSON] and [PERSON] [16] highlight the importance of providing a clear and structured context to optimize interactions with LLMs. In this sense, the clarity and specificity of prompts are presented as key factors in obtaining more accurate outputs. Nonetheless, while previous studies have examined these strategies in expert-designed environments, our research focused on the spontaneous interactions between students and ChatGPT, offering a closer perspective on the real-world use of these tools in higher education contexts. On the other hand, the SPS (Strategies with Purpose Specification) cluster grouped two techniques: CT5 (Goal or Purpose Indicator) and RBT19 (Request with Purpose), both of which demonstrated a significant association with response quality, F (1, 67) = 8.58, \(p\) = 0.005, R\({}^{2}\) = 0.114, \(\beta\) = 0.337. However, the observed effect was smaller than that of the GCS cluster, suggesting that while explicitly articulating the intended purpose in prompts can improve interactions with generative AI, its effective implementation may require a higher level of linguistic proficiency and a deeper understanding of LLMs' operational principles. These results are consistent with the systematic review conducted by [PERSON] and [PERSON] [37], who emphasize the importance of users clearly defining their objectives when interacting with AI to maximize effectiveness. Nonetheless, their study revealed that students encounter difficulties in implementing these strategies, suggesting the need for more specialized training in prompt engineering. Building on this discussion, Figure 3 presents a synthesis of the findings from this study, summarizing the relationships between the identified strategies and their impact on response quality. The findings of this study indicate that the techniques grouped within GCS and SPE align with certain prompt engineering strategies documented in the literature. In particular, the techniques classified under GCS share multiple characteristics with the few-shot prompting strategy [21; 38; 39]. According to [PERSON] [40], incorporating examples and contextual information within a prompt can significantly enhance the quality of responses generated by LLMs. Additionally, [PERSON] and [PERSON] [41] highlight that prompts enriched with prior contextual information are more likely to generate meaningful responses.