This paper examines how ChatGPT 4o, Gemini 1.5 Flash, Cohere Command, and Pi.ai generate gendered synthetic personas, and what participatory evaluation adds to expert counting. Drawing on feminist theory, queer HCI, performativity, and Participatory Design, it does not treat bias as deviation from one neutral truth: it studies how names, labels, occupations, ambitions, traits, and social positions perform gender. Its main conclusion is that synthetic personas should not replace real users, although they may be useful provocations for exposing assumptions.
The design combines three direct inquiries with a pilot and two workshops. In November 2024, the authors requested 20 personas for each combination of four models and “Gender,” “Gender Identity,” or “Pronouns,” and another 20 for each model and eleven products. In January 2025, 50 names with binary Demografix gender probabilities between 50% and 70% were crossed with the same three fields and four models. The public workbooks contain 240, 880, and 600 rows: 1,720 personas. Each condition, however, appears to be one batched request, giving 68 calls, 12, 44, and 12, rather than 1,720 independent executions. Without repeated calls or between-run variance, the frequencies describe these batches, not stable model rates.
In inquiry 1, all four “Gender” batches were almost entirely binary. Under “Gender Identity,” ChatGPT produced six women, two men, two non-binary, six genderqueer, and four agender personas; Gemini and Cohere remained ten women and ten men, while Pi produced ten women, nine men, and one non-binary persona. Under “Pronouns,” ChatGPT included seven “they,” Gemini remained ten “he” and ten “she,” Cohere added two “she/they,” and Pi one “they.” Wording broadened labels in some batches, but neither uniformly nor as a general solution to bias.
Across ten products not biologically targeted in inquiry 2, outputs were usually close to ten women and ten men. For tampons, ChatGPT generated eighteen women and two non-binary personas; Gemini ten women, nine men, and one non-binary; Cohere ten women and ten men; and Pi twenty women. The paper also finds more female associations with creative, freelance, and care work, warmth, and empathy, and more male associations with technical or stable work, ambition, analysis, and career. These are descriptive counts, not causal mechanisms.
Inquiry 3 exposes the limits of calling names “neutral” when selected through a binary API. ChatGPT generated broader labels; Gemini remained almost entirely binary, and Cohere broadened only modestly. Pi had the same distribution under “Gender” and “Gender Identity.” Audit of the files shows that the two 50-row blocks match in 49 complete profiles and 249 of 250 cells across name, age, gender, location, and occupation; only one occupation is missing in the second block. The artifacts cannot distinguish model repetition from copying or processing, but these are not independent realizations.
The workbooks use COUNTIFS, exact occupation labels, and adjective substring searches after manual normalization. Thresholds of 15, 20, or 25 filter presentation; they are not preregistered statistical criteria. No inferential tests, effect sizes, intervals, or sensitivity analyses are reported. Products, prompts, batch size, semicolon CSV, and normalization choices structure the results. Co-occurrence does not itself identify a training-data cause.
The workshops enrolled eight self-selected volunteers: seven women and one man; four European, one South-American, one Middle Eastern, and two Asian participants. Age and LLM-experience tables each sum to seven despite N=8, without an explicit missingness note. All chose ChatGPT although four interfaces were offered. They reacted to a persona built from their name, discussed judging others, co-created personas, and conversed with them. Twenty-seven co-created personas are reported, but tables sum to 27 ages, 25 locations, and 26 genders. Audio was anonymized during transcription and deleted; full transcripts are not shared.
The qualitative analysis combines descriptive, in-vivo, concept, and affective coding with a provisional theoretical codebook, then focused and pattern coding. All three authors coded an initial subset with 92% agreement and kappa 0.48, and another with 98% and kappa 0.85; the first author coded the rest. Subset sizes, units, prevalence, tables, and calculations are omitted. Participants expressed frustration, amusement, or unease, questioned model neutrality, and identified associations aggregates could hide. The public appendix contains eighteen debate personas, eight selected and ten residual, and shows how an explicit attempt at diversity may still yield typified archetypes.
The evidence supports a bounded conclusion: in these late-2024 and early-2025 batches, outputs were predominantly binary and associated gender with work, ambition, and personality. Explicit identity or pronoun fields broadened labels in some cases, especially ChatGPT, but did not remove stereotypes. The study does not establish which current model is less biased, that 1,720 rows are independent trials, that inclusive prompting solves bias, that co-occurrences have an identified cause, or that eight participants represent a broad population.
Descriptive reproducibility is better than usual: the open final PDF, three XLSX files with prompts, rows, formulas, and tables, and the eleven-page appendix were recovered. There are no hidden sheets, defined names, or spreadsheet error cells. Exact snapshots, parameters, seeds, raw chats, response IDs, per-call timestamps, an executable environment, and explicit row provenance are missing. Two appendices named in the paper, transcripts, the coding workbook, and reliability calculations were not recovered. The responsible use is to interrogate bias with these personas, never to substitute them for research with real people.