## 4 Discussion
### Implications of This Work in the Field of Higher Education  
As Holmes [43] suggests, it is important to recognize that the connections between AI and education are more complex than they might seem, with misunderstandings often arising due to a lack of investigations [44]. With this work, we propose that both the strategies for formulating effective questions to interact with an LLM and the quality of the responses these LLMs provide depends on students' grammatical performance. While much research focuses on prompt engineering strategies, it is important that literacy policies for the use of AI in education also incorporate the development of students' grammatical performance. Figure 3 represents a methodological proposal that educational communities could use to enhance students' grammatical performance, enabling responsible interaction between students and LLMs. This framework highlights four essential aspects to consider in human-machine interaction: (i) grammatical performance; (ii) grammatical strategies for question formulation; (iii) interaction with the LLM prompt; and (iv) evaluating response quality. Grammatical performance, focused on the verb and its mood, influences sentence complexity. The subjunctive mood, being more complex than the indicative, presents greater challenges for non-native speakers. Compound sentences, especially subordinate ones, increase complexity by relating multiple ideas. According to our study, the choice of verbal mood and the type of sentence are linked to the length of interactions with the LLMs. Speakers with more expertise, those who are able to combine different moods and sentence types, can generate longer and more informative messages, achieving better results in their interactions with the LLMs. Hence, the speaker's expertise in structuring sentences is positively related to better outcomes and success in interactions with LLMs, such as ChatGPT. Grammatical strategies for question formulation can be considered part of the prompt engineering field. A prompt is an input to a generative AI model used to guide its output [45; 46]. Many studies demonstrate that more effective prompts improve the quality of responses across a wide range of tasks [47]. However, understanding how to use it correctly remains an emerging field, with various terms and techniques that are not necessarily well understood by students. To evaluate the quality of responses, it is crucial that students also enhance their skills, such as critical thinking, researching, and contrasting information sources, while being able to analyze the ethical aspects and intellectual property rights involved in interactions with AI. Moreover, considering the exploratory nature of this study and its focus on Spanish language interaction with LLMs, the findings may serve as a foundation for informing future educational policies. In particular, the proposed methodological framework--highlighting the importance of grammatical competence, prompt formulation, and response evaluation--could support the development of digital AI literacy programs in higher education. These programs might guide both students and educators in cultivating critical and effective AI use, especially in multilingual and underrepresented contexts. However, further empirical validation is required before these recommendations can inform formal policy initiatives. Finally, we can observe that all the proposed components are connected to grammatical performance. We intend to emphasize the importance of grammatical performance, as a foundational step in improving interactions with AI. In addition, the findings of this  
Figure 3: Methodological framework for interacting with LLMs. This figure presents a proposed methodology for structuring interactions with LLMs, outlining key components and processes involved. The direction of the rows indicates the interaction and influence of grammatical performance on all framework components.