The Implications of Generative-AI User Prompt Engineering Shaping the Absent Presence of Latino Women Textual Rhetoric in LLMs
Proposal Type
Individual Talk
Location
Narratives & Worlds
Start Date
July 2026
End Date
July 2026
Abstract
This article examines how large language models (LLMs) generate text that affects underrepresented groups, particularly Latino women, due to limited cultural, contextual, and abstraction training rooted in biases. The research draws on Brock’s (2018) CTDA theory, White et al.’s (2023) prompt engineering framework, Klein’s (2015) computational spatiality, and Anzaldua’s (1987) concepts of cultural, emotional, and gendered borderlands to analyze the outputs. Using narrative prompt experiments, Chat GPT 3.5, 5, Claude AI, Gemini, and Microsoft Co-Pilot produced changes in their textual outputs as evidence of further algorithmic designs. Nevertheless, continual biases embedded in the algorithms’ linguistic representation and misrepresentation through the absence of contextual and abstraction interpretations, and profession stereotypes persisted. Despite the technologies’ design advancement, evidence of the limitations of Latina characters were consistently represented through tropes of immigration, Spanglish use, and food consumption, and generational cultural transference in the kitchen spatiality of the home. I argue that LLMs training relies on cultural, political, social, and economic worldviews that have historically been rooted in harmful biases while new data is shaped by biases in general. The study aims to enhance LLMs' algorithmic effect in shaping the perception of cultural and its limitations to remove bias through continued research and development. Hence, this research benefits program designers, educators, writers, and students.
The Implications of Generative-AI User Prompt Engineering Shaping the Absent Presence of Latino Women Textual Rhetoric in LLMs
Narratives & Worlds
This article examines how large language models (LLMs) generate text that affects underrepresented groups, particularly Latino women, due to limited cultural, contextual, and abstraction training rooted in biases. The research draws on Brock’s (2018) CTDA theory, White et al.’s (2023) prompt engineering framework, Klein’s (2015) computational spatiality, and Anzaldua’s (1987) concepts of cultural, emotional, and gendered borderlands to analyze the outputs. Using narrative prompt experiments, Chat GPT 3.5, 5, Claude AI, Gemini, and Microsoft Co-Pilot produced changes in their textual outputs as evidence of further algorithmic designs. Nevertheless, continual biases embedded in the algorithms’ linguistic representation and misrepresentation through the absence of contextual and abstraction interpretations, and profession stereotypes persisted. Despite the technologies’ design advancement, evidence of the limitations of Latina characters were consistently represented through tropes of immigration, Spanglish use, and food consumption, and generational cultural transference in the kitchen spatiality of the home. I argue that LLMs training relies on cultural, political, social, and economic worldviews that have historically been rooted in harmful biases while new data is shaped by biases in general. The study aims to enhance LLMs' algorithmic effect in shaping the perception of cultural and its limitations to remove bias through continued research and development. Hence, this research benefits program designers, educators, writers, and students.
Bio
Angely Suarez DeJesus has been teaching 10th grade high school English for 4 years. She received a BA in Sociology at The University at Buffalo in 2008, with a background in Architectural Studies & Design at Morrisville State College. She completed the MA Educational Leadership Program College Teaching/ Higher Education Track at UCF in Spring 2024. She was a recipient in 2024 of the Summer Mentoring Fellowship award, for which her research focused on how AI enhances and minimizes Latino and female rhetoric in the field of rhetoric and composition through using a network mapping methodology on academic forum abstracts. The larger scope of her research interests lie within digital humanities, entailing the exploration of how LLM technologies affect Latino women through textual, visual, and digital rhetoric. Other areas of interest are in the field of technical professional communication, digital media, and rhetoric and composition.