A1 Refereed original research article in a scientific journal
Demographics do not matter? : Exploring the impact of gender and ethnicity on users’ identification with AI-generated personas
Authors: Kaate, Ilkka; Salminen, Joni; Jung, Soon-Gyo; Santos, Joao; Aldous, Kholoud; Häyhänen, Essi; Azem, Jinan; Bernard, J Jansen
Publisher: Elsevier
Publishing place: LONDON
Publication year: 2025
Journal: International Journal of Human-Computer Studies
Journal name in source: International Journal of Human-Computer Studies
Journal acronym: INT J HUM-COMPUT ST
Article number: 103548
Volume: 202
Number of pages: 14
ISSN: 1071-5819
eISSN: 1095-9300
DOI: https://doi.org/10.1016/j.ijhcs.2025.103548
Web address : https://doi.org/10.1016/j.ijhcs.2025.103548
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/477691407
Demographics are considered foundational information in most persona profiles. However, the effect of persona ethnicity and gender on designers' identification with the persona has limited evaluation in the human-computer interaction literature. We conducted a study with 64 professional designers from the United States, Indian, Korean, and Mexican nationalities to investigate the effects of AI-generated persona ethnicity and gender on persona identification. The personas were created using Generative AI in the persona narratives and the persona video creation. The contribution of this work is that, against assumptions, neither persona ethnicity nor gender play a major role in persona identification among designers with different ethnic backgrounds. While there were some insinuations of ethnicity and gender in the open-ended feedback from the designers, the emergent qualitative themes describing persona identification were overwhelmingly universal and applicable regardless of ethnicity or gender. This implies that professional designers can effectively use personas with different demographic backgrounds, and effects of demographic attributes in personas leading to stereotyping are less impactful than presumed.
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