Analyzing Higher Education Students’ Prompting Techniques and Their Impact on ChatGPT’s Performance: An Exploratory Study in Spanish

Abstract

Generative artificial intelligence is reshaping how people interact with digital technologies, emphasizing the need to develop effective skills for engaging with it. In this context, prompt engineering has emerged as a critical skill for optimizing AI-generated outputs. However, research on how higher education students interact with these technologies remains limited, particularly in non-English-speaking contexts. This exploratory study examines how 102 higher education students in Chile formulated prompts in Spanish and how their techniques influenced the responses generated by ChatGPT (free version 3.5). A quantitative analysis was conducted to assess the relationship between prompt techniques and response quality. Two emergent prompt engineering strategies were identified: the Guide Contextualization Strategy and the Specific Purpose Strategy. The Guide Contextualization Strategy focused on providing explicit contextual information to guide ChatGPT’s responses, aligning with few-shot prompting, while the Specific Purpose Strategy emphasized defining the request’s purpose, aligning with structured objective formulation strategies. The regression analysis indicated that the Guide Contextualization Strategy had a greater impact on response quality, reinforcing the importance of contextual information in effective interactions with large language models. As an exploratory study, these findings provide preliminary evidence on prompt engineering strategies in Spanish, a relatively unexplored area in artificial intelligence education research. Based on these results, a methodological framework is proposed, encompassing four key dimensions: grammatical skills; prompt strategies; response from the large language model; and evaluation of response quality. This framework lays the groundwork for future artificial intelligence digital literacy interventions, fostering critical and effective engagement with generative artificial intelligence while also highlighting the need for further research to validate and expand these initial insights.

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