## 6 Conclusions  
This exploratory study provides evidence on the impact of prompt engineering strategies on the quality of responses generated by ChatGPT (free version 3.5) in Spanish. Analyzing the techniques used by 102 higher education students, two emergent strategies were identified: the Guide Contextualization Strategy (GCS) and the Specific Purpose Strategy (SPS). The first strategy emphasizes the use of contextual information to guide response generation, while the second focuses on the explicit formulation of the request's purpose.  
The results indicate that GCS is more strongly associated with response quality, reinforcing the importance of providing a clear contextual framework to enhance the precision and relevance of interactions with GenAI models. On the other hand, the lower impact of SPS suggests that simply stating the purpose of a request is insufficient unless students also possess a deeper understanding of how LLMs function. This highlights a potential gap in AI literacy, where users may struggle to translate well-defined objectives into effective prompt formulations.  
These findings align with prior research on few-shot prompting, persona prompting, and structured objective-setting strategies in interactions with GenAI. However, a key distinction of this study lies in its focus on spontaneous student interactions with ChatGPT, as opposed to controlled environments led by AI experts. This methodological approach offers a closer approximation to the real world.  
AI usage in higher education, capturing how students naturally engage with LLMs in academic settings. The results suggest that, while context-driven strategies align with established prompting techniques, students may require additional support to refine their use of structured request-based approaches.  
From an educational perspective, these findings reinforce the importance of developing AI digital literacy programs that extend beyond technical knowledge of GenAI models and equip students with effective prompt engineering strategies. The methodological framework proposed in this study, encompassing four key dimensions--grammatical skills; prompt strategies; response from the LLM; and evaluation of response quality--serves as a foundation for future initiatives aimed at fostering critical and effective engagement with GenAI in Spanish-speaking contexts.  
As an exploratory study, this research underscores the need for further investigation into the relationship between GenAI and prompt engineering performance, considering factors such as language proficiency, users' disciplinary backgrounds, and the impact of AI digital literacy programs. Additionally, future research should examine how these strategies manifest across different languages and educational settings and assess the effectiveness of interventions designed to enhance students' AI interaction skills. Given the preliminary nature of our findings, we emphasize the importance of replicating this study in broader and more diverse educational contexts to validate the results and explore their applicability at scale. Ultimately, these insights contribute to a growing understanding of how learners engage with AI in academic contexts, reinforcing the need for tailored educational strategies that bridge the gap between technological advancement and pedagogical practice.