###### Abstract
These benchmarks focus on task-specific capabilities but overlook the influence of linguistic and grammatical complexity in user-generated prompts, particularly in non-English languages. Given this gap in the literature, we adopted an exploratory approach to investigate how grammatical abilities affect response quality, aiming to identify trends and generate hypotheses rather than to generalize findings. By addressing this underexplored area, the present study contributes to the understanding of human-AI interaction in diverse linguistic contexts. Non-English languages pose unique challenges for LLMs. For instance, Spanish includes grammatical constructs like the subjunctive mood, which has no direct equivalent in English and plays a critical role in expressing hypothetical or uncertain situations. Despite advances in multilingual training [18], questions remain regarding how well LLMs can interpret prompts written in languages with such complexities. This gap in research highlights the need for studies that explore the interaction between grammatical characteristics in non-English prompts and the quality of LLM responses. While this study does not focus on the internal workings of LLM architectures, it is essential to consider how linguistic features of Spanish might impact model performance. Unlike English, Spanish exhibits unique grammatical characteristics, including the use of the subjunctive mood, complex verb conjugation patterns, and flexible word order. Thesestructural differences can pose challenges for LLMs, particularly when models are primarily trained on English language data. Recent studies [19; 20] have highlighted that models may struggle with languages underrepresented in training datasets, potentially leading to ambiguity or shifts in semantic interpretation. Addressing how these grammatical complexities influence AI-generated responses in Spanish-speaking contexts contributes to advancing the field of multilingual AI and improving prompt design strategies for non-English languages. This exploratory study addresses this gap by focusing on higher education students in Chile. This demographic was chosen due to their familiarity with technology and their ability to engage effectively with the data collection methodology [21; 22; 23]. Exploring how these students write prompts in Spanish and how those prompts influence LLM responses provides valuable insights into improving human-machine interaction and enhancing the educational use of generative AI tools. This manuscript makes several significant contributions to the field of human-AI interaction and generative language models. It addresses a novel and underexplored question regarding the relationship between grammatical ability in Spanish and the quality of responses generated by LLMs, focusing on how linguistic and cultural nuances may influence AI performance. By conducting a single-scenario analysis using the 3.5 free version of ChatGPT within a Spanish-speaking context, the study provides foundational insights into how linguistic characteristics impact LLM outputs, laying the groundwork for future multilingual comparisons. Additionally, the study introduces a protocol for evaluating the grammatical abilities of neurotypical adults, offering a structured methodological approach that ensures data reliability and serves as a reference for similar linguistic assessments. The findings also have significant educational implications, as they highlight key considerations for enhancing the use of generative AI tools in academic settings, guiding students toward effective prompt writing and addressing gaps in linguistic training for human-AI interaction. Finally, the manuscript proposes a comprehensive methodological framework for educational communities aimed at improving students' grammatical performance, emphasizing four critical aspects of human-machine interaction and providing practical recommendations for integrating AI tools into learning environments.