###### Abstract  
Generative Artificial Intelligence (AI) has transformed personal and professional domains by enabling creative content generation and problem-solving. However, the influence of users' grammatical abilities on AI-generated responses remains unclear. This exploratory study examines how language and grammar abilities in Spanish affect the quality of responses from ChatGPT (version 3.5). Despite the robust performance of Large Language Models (LLMs) in various tasks, they face challenges with grammatical moods specific to non-English languages, such as the subjunctive in Spanish. Higher education students were chosen as participants due to their familiarity with AI and its potential use in learning. The study assessed ChatGPT's ability to process instructions in Chilean Spanish, analyzing how linguistic complexity, grammatical variations, and informal language impacted output quality. The results indicate that varied verbal moods and complex sentence structures significantly influence prompt evaluation, response quality, and response length. Based on these findings, a framework is proposed to guide higher education communities in promoting digital literacy and integrating AI into teaching and learning. 2020 IEEE/RSJ International Conference on Intelligent Systems and Applications (ICAS)  
aligned with user intent. Prompt engineering evaluates how LLMs handle variability, ambiguity, and context shifts in prompts [9]. For example, benchmarks like BIG-bench [10] test LLMs' ability to infer grammatical roles of pseudo-words based on orthographic and phonetic patterns, while studies like Singh et al. (2024) [8] assess robustness to linguistic errors using corrupted datasets. Despite these advances, existing research has predominantly addressed robustness to syntactic variability and ambiguity, particularly in chatbot interactions where advanced syntactic analysis is applied to enhance accuracy and contextual understanding [11]. Additionally, some studies have investigated robustness to grammatical and orthographic errors, especially in contexts such as discourse analysis for aphasic speech, where identifying intended lexical targets poses significant challenges [12]. In the domain of sentiment analysis, robustness has been explored from the perspective of non-standard and low-resource linguistic inputs, aiming to improve response consistency even with minimal training data [13]. Together, these studies highlight the challenges that LLMs face when processing linguistic variability and error management, particularly in non-English contexts where morphosyntactic complexity plays a critical role. While considerable progress has been made in enhancing LLM robustness to linguistic variability and error management, most research has predominantly focused on English and a few major languages. Multilingual AI research often faces the challenge of balancing performance across diverse languages, particularly those with complex grammatical structures and regional variations, such as Spanish. Theoretical frameworks for multilingual NLP highlight the need to address not only lexical diversity but also morphosyntactic variability, as seen in the rich verbal conjugation systems and pragmatic nuances of Chilean Spanish. Addressing these challenges requires a deeper understanding of how grammatical complexity influences AI responses, especially in generative models. This study contributes to filling this gap by exploring the impact of grammatical abilities in Spanish on the quality of LLM-generated outputs, thereby advancing the theoretical foundations of multilingual AI robustness. However, most existing evaluations of LLMs rely on predefined benchmarks such as the Massive Multitask Language Understanding (MMLU) for multitask accuracy [14], the MATH for mathematical reasoning [15], the HumanEval for coding performance [16], and the Multilingual Grade School Math (MGSM) for multilingual capabilities [17].