Representations of gender and sexuality in the age of GenAI: pedagogical approaches to the mediatization of the collective imaginary

Abstract

This study examines cultural biases in generative AI, with a particular focus on representations of gender and
sexuality. Analyses of interactions with chatbots conducted through six university workshops (N=245) produced
1,820 traces, which constitute the analytical corpus used to measure bias levels before and after the drafting and
finalization of documents, as well as to monitor process indicators. Bias decreased across all categories, particularly
with regard to gender (31.8% 16.3%) and certain cultural representations (22.0%→12.7%). To correct bias,
groups employed an average of four prompts; a higher number of prompts was positively associated with residual
bias (OR=1.42), revealing what we define as the fatigue paradox, with satisfaction with the final output negatively
correlated with the number of iterations performed (ρ≈−.16). Qualitative analysis shows frequent lexical cleanups but limited structural reallocation of agency; explicit mentions of trans* or non-binary identities are rare
(≈1.2%).