Prompt taxonomy for optimize academic accessibility in conversational Artificial Intelligence systems
DOI:
https://doi.org/10.59471/raia2025216Keywords:
conversational artificial intelligence, higher education, transformers, prompting, accessibility, taxonomyAbstract
The growing integration of conversational Artificial Intelligence in higher education is generating emerging demands for digital literacy, including the development of competencies to formulate effective prompts in natural language. This work approaches accessibility as a sociotechnical necessity arising from the lack of socially available competencies to interact with emerging technologies. In this sense, improving accessibility involves strengthening capacities that enable any user to fully benefit from the potential of conversational AI. The central aim of the study is to construct a prompt taxonomy that classifies the structural, functional, contextual, and expressive properties of these textual units. From an applied perspective, the research takes interactions with ChatGPT at the Universidad Abierta Interamericana as a reference case, with the goal of providing a conceptual foundation from which to derive prompting strategies oriented toward academic practice. Preliminary results include a matrix of dimensions, variables, and indicators developed through a recent literature review and discussions within the research team. These advances constitute a basis for strengthening digital literacy in university settings and promoting a more meaningful and equitable use of conversational AI.
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