Gender attribution in generative artificial intelligence: a comparative study and implications for higher education
DOI:
https://doi.org/10.36825/RITI.14.34.012Keywords:
Generative Artificial Intelligence, Gender Bias, Generic Masculine, Large Language Models, Higher EducationAbstract
The integration of generative artificial intelligence (AI) in higher education requires examining how these systems represent gender. This study analyses gender attribution in the responses of five systems (ChatGPT, Claude, Gemini, Grok and Perplexity) to 16 controlled prompts about professional roles, organised into eight categories. In 40 responses to prompts without gender cues, we examined whether systems assigned a gender to the professional referent; in 39 responses to explicitly gendered prompts, we analysed differential treatment and the reproduction of stereotypes. Coding followed an explicit protocol, was assisted by a language model, and was validated through independent human coding of a subsample (κ = 0.87 between human coders). For gender-neutral prompts, 55.0 % of responses attributed no gender, but when gender was marked, masculine marking predominated (40.0 %), mainly through the generic masculine, compared with a single feminine attribution (2.5 %). Differences between systems did not reach statistical significance with this sample size. In gendered scenarios, no system favoured the male candidate, three responses recommended the female candidate, and one system gave contradictory assessments depending on the prompt version. The results reveal patterns consistent with an androcentric default and highlight the need for critical AI literacy in higher education.
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