Gendered Professional Identity in AI-Generated Discourse
An Exploratory Corpus-Assisted Analysis of ChatGPT, Gemini, and Grok
Keywords:
Large Language Models; Corpus-Assisted Critical Discourse Analysis; Gender Representation; Professional Identity; Critical Discourse Analysis; Systemic Functional LinguisticsAbstract
As large language models (LLMs) become increasingly prevalent in professional and organizational communication, concern has shifted from overt stereotypes to subtler patterns through which AI-generated language may reproduce gendered assumptions. This study examines how ChatGPT, Gemini, and Grok construct male- and female-referenced professional identities across ten occupations. The dataset comprises 60 texts, generated in June 2025 through three LLM models (ChatGPT, Gemini, and Grok). Using corpus-assisted critical discourse analysis, the study combines keyword and Key Word in Context (KWIC) analysis in Sketch Engine with Halliday and Matthiessen’s systemic functional linguistics, Fairclough’s three-dimensional model, and van Leeuwen’s social actor representation framework. In the sampled outputs, overt stereotyping was limited; however, recurrent lexical and grammatical differences associated male professionals more often with institutional authority and strategic action, whereas female professionals were more often associated with relational competence and communicative expertise. Because each prompt was generated only once during a single collection session. The results show that explicit stereotyping has been visibly reduced in all three LLMs. Nevertheless, implicit ideologies regarding gender still exist in today’s AI-generated professional discourse where male professionals have been predominantly associated with institutional authority and strategic leadership, while female professionals have been characterized through relational competence and communicative expertise.
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Copyright (c) 2026 Marriam Bibi, Dr. Urooj Fatima Alvi (Author)
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