Helena A. Haxvig, Vincenzo D’Andrea, Maurizio Teli
pp. 15 – 37, download
(https://doi.org/10.55612/s-5002-068-001)
Abstract
In this conceptual paper, we synthesize findings from four studies at the intersection of Participatory Design (PD) and generative AI to articulate a coupled path: PD is suited for evaluating AI through lived experience; insights inform AI-supported co-creation (e.g., enacted/synthetic personas); and PD, in turn, designs and validates these tools. We show that AI-generated and -enacted personas can widen perspective but are not recommended as substitutes for people. Based on the cross-cutting lessons learned from the four studies we outline strategies and care practices for responsible use of synthetic personas, introducing a layered bidirectional model illustrating how PD values should form use of these instruments while knowledge acquired from use can in turn inform reconfigurations of PD practices and values. This strategy positions PD as both shaper and steward of generative AI in design.
Keywords: Participatory Design, AI, Generative AI, Synthetic Personas, Co-Creation, Evaluation, Ethics, Interactive Personas, Bias..
References
1. Buolamwini J.: Unmasking AI: my mission to protect what is human in a world of machines, Random House, New York, (2023)
2. Crawford K.: Atlas of AI: power, politics, and the planetary costs of artificial intelligence, Yale University Press, New Haven, (2021) . https://doi.org/10.2307/j.ctv1ghv45t
3. Eubanks V.: Automating inequality: how high-tech tools profile, police, and punish the poor, Picador St. Martin’s Press, New York, (2019)
4. O’Neil C.: Weapons of math destruction: how big data increases inequality and threatens democracy, Penguin Books, London, (2017)
5. Baumer E.P.: Toward human-centered algorithm design Big Data Soc., 4, pp. 2053951717718854 (2017) . https://doi.org/10.1177/2053951717718854
6. Miceli M., Posada J., Yang T.: Studying up machine learning data: Why talk about bias when we mean power? Proc. ACM Hum.-Comput. Interact., 6, pp. 1–14 (2022) . https://doi.org/10.1145/3492853
7. Riedl M.O.: Human-centered artificial intelligence and machine learning Hum. Behav. Emerg. Technol., 1, pp. 33–36 (2019) . https://doi.org/10.1002/hbe2.117
8. Cherrington M., Airehrour D., Lu J., Xu Q., Cameron-Brown D., Dunn I.: Features of Human-Centred Algorithm Design 2020 30th International Telecommunication Networks and Applications Conference (ITNAC). pp. 1–6 (2020) .
https://doi.org/10.1109/ITNAC50341.2020.9315169
9. Papakyriakopoulos O., Watkins E.A., Winecoff A., Jaźwińska K., Chattopadhyay T.: Qualitative Analysis for Human Centered AI ArXiv Prepr. ArXiv211203784, (2021)
10. Benjamin R.: Race After Technology Social Theory Re-Wired. Routledge (2023)
11. Winograd T.: Categories, disciplines, and social coordination Comput. Support. Coop. Work CSCW, 2, pp. 191–197 (1993) . https://doi.org/10.1007/BF00749016
12. Winograd T., Flores F., Winograd T.: Understanding computers and cognition: a new foundation for design, Ablex Publ. Corp, Norwood, NJ, (1986)
13. Suchman L.: Do categories have politics? Comput. Support. Coop. Work CSCW, 2, pp. 177–190 (1993) . https://doi.org/10.1007/BF00749015
14. Scheuerman M.K., Hanna A., Denton E.: Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development Proc. ACM Hum.-Comput. Interact., 5, pp. 317:1-317:37 (2021) . https://doi.org/10.1145/3476058
15. Bødker S., Kyng M.: Participatory Design that Matters—Facing the Big Issues ACM Trans Comput-Hum Interact, 25, pp. 4:1-4:31 (2018) . https://doi.org/10.1145/3152421
16. Ehn P.: Participation in design things Proceedings of the Tenth Anniversary Conference on Participatory Design 2008. pp. 92–101. Indiana University, USA (2008)
17. Simonsen J., Robertson T. eds: Routledge international handbook of participatory design, Routledge, London, (2013) . https://doi.org/10.4324/9780203108543
18. Smith R.C., Loi D., Winschiers-Theophilus H., Huybrechts L., Simonsen J. eds: Routledge International Handbook of Contemporary Participatory Design, (2024) . https://doi.org/10.4324/9781003334330
19. Stigberg S.K., Carcani K., Joshi S.G., Bratteteig T.: Participatory Design meets Artificial Intelligence: Co-imagining mutual learning of AI technologies and designing with AI tools Adjunct Proceedings of the 2024 Nordic Conference on Human-Computer Interaction. pp. 1–3. ACM, Uppsala Sweden (2024) .
https://doi.org/10.1145/3677045.3685471
20. Hermann J., Jansen N., Dogangün A.: We Need to Understand Where the Data Comes From: Co-Designing Transparent AI Systems for Caregiving Companion Proceedings of the 30th International Conference on Intelligent User Interfaces. pp. 105–109. Association for Computing Machinery, New York, NY, USA (2025) . https://doi.org/10.1145/3708557.3716354
21. Zytko D., J. Wisniewski P., Guha S., P. S. Baumer E., Lee M.K.: Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems. pp. 1–4. Association for Computing Machinery, New York, NY, USA (2022) . https://doi.org/10.1145/3491101.3516506
22. Bratteteig T., Verne G.: Does AI make PD obsolete?: exploring challenges from artificial intelligence to participatory design Proceedings of the 15th Participatory Design Conference: Short Papers, Situated Actions, Workshops and Tutorial – Volume 2. pp. 1–5. ACM, Hasselt and Genk Belgium (2018) . https://doi.org/10.1145/3210604.3210646
23. Delgado F., Yang S., Madaio M., Yang Q.: The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization. pp. 1–23. Association for Computing Machinery, New York, NY, USA (2023) . https://doi.org/10.1145/3617694.3623261
24. Cai A., Rick S.R., Heyman J.L., Zhang Y., Filipowicz A., Hong M., Klenk M., Malone T.: DesignAID: Using Generative AI and Semantic Diversity for Design Inspiration Proceedings of The ACM Collective Intelligence Conference. pp. 1–11. Association for Computing Machinery, New York, NY, USA (2023) . https://doi.org/10.1145/3582269.3615596
25. Chiou L.-Y., Hung P.-K., Liang R.-H., Wang C.-T.: Designing with AI: An Exploration of Co-Ideation with Image Generators Proceedings of the 2023 ACM Designing Interactive Systems Conference. pp. 1941–1954. Association for Computing Machinery, New York, NY, USA (2023) . https://doi.org/10.1145/3563657.3596001
26. Popova V.: Co-creating Futures for Integrating Generative AI into the Designers’ Workflow, (2023)
27. Freese S.: AI in Co-Creation : The usability and impact of AI tools for co- creation in participatory design to generate innovative and user- centric design solutions, (2023)
28. van den Broek S., Sankaran S., de Wit J., de Rooij A.: Exploring the Supportive Role of Artificial Intelligence in Participatory Design: A Systematic Review Proceedings of the Participatory Design Conference 2024: Exploratory Papers and Workshops – Volume 2. vol. 2. pp. 37–44. Association for Computing Machinery, New York, NY, USA (2024) . https://doi.org/10.1145/3661455.3669868
29. Bratteteig T., Wagner I.: Disentangling power and decision-making in participatory design Proceedings of the 12th Participatory Design Conference: Research Papers – Volume 1. pp. 41–50. ACM, Roskilde Denmark (2012) .
https://doi.org/10.1145/2347635.2347642
30. Joshi S.G., Tolloczko C.H., Wenaas S., Holm O.J., Langved M.L.F.: Investigating How Generative AI Affects Decision-Making in Participatory Design: Shifting the space to make design choices Nordic Conference on Human-Computer Interaction. pp. 1–14. ACM, Uppsala Sweden (2024) . https://doi.org/10.1145/3679318.3685384
31. Nielsen L.: Personas The Encyclopedia of Human-Computer Interaction. Interaction Design Foundation – IxDF (2014)
32. Salminen J., Guan K., Jung S.-G., Jansen B.J.: A Survey of 15 Years of Data-Driven Persona Development Int. J. Human–Computer Interact., 37, pp. 1685–1708 (2021) . https://doi.org/10.1080/10447318.2021.1908670
33. De Paoli S.: Improved prompting and process for writing user personas with LLMs, using qualitative interviews: Capturing behaviour and personality traits of users, https://ui.adsabs.harvard.edu/abs/2023arXiv231006391D, (2023), .
https://doi.org/10.48550/arXiv.2310.06391
34. Holzinger A., Kargl M., Kipperer B., Regitnig P., Plass M., Müller H.: Personas for Artificial Intelligence (AI) an Open Source Toolbox IEEE Access, 10, pp. 23732–23747 (2022) . https://doi.org/10.1109/ACCESS.2022.3154776
35. Jansen B.J., Salminen J., Jung S., Guan K.: Data-Driven Personas, Springer Nature, (2022)
36. Jung S.-G., Salminen J., Aldous K.K., Jansen B.J.: PersonaCraft: Leveraging language models for data-driven persona development Int. J. Hum.-Comput. Stud., 197, pp. 103445 (2025) . https://doi.org/10.1016/j.ijhcs.2025.103445
37. Salminen J., Liu C., Pian W., Chi J., Häyhänen E., Jansen B.J.: Deus Ex Machina and Personas from Large Language Models: Investigating the Composition of AI-Generated Persona Descriptions Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. pp. 1–20. Association for Computing Machinery, New York, NY, USA (2024) . https://doi.org/10.1145/3613904.3642036
38. Blythe M.A., Wright P.C.: Pastiche scenarios: Fiction as a resource for user centred design Interact. Comput., 18, pp. 1139–1164 (2006) .
https://doi.org/10.1016/j.intcom.2006.02.001
39. Jansen B.J., Jung S.-G., Nielsen L., Guan K.W., Salminen J.: How to Create Personas: Three Persona Creation Methodologies with Implications for Practical Employment Pac. Asia J. Assoc. Inf. Syst., 14, pp. 1–28 (2022) . https://doi.org/10.17705/1pais.14301
40. Prpa M., Troiano G.M., Wood M., Coady Y.: Challenges and Opportunities of LLM-Based Synthetic Personae and Data in HCI Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems. pp. 1–5. Association for Computing Machinery, New York, NY, USA (2024) .
https://doi.org/10.1145/3613905.3636293
41. Haxvig H.A., D’Andrea V., Teli M.: “I’ve never seen a glass ceiling better represented”: Bias and gendering in LLM-generated synthetic personas from a participatory design perspective Int. J. Hum.-Comput. Stud., 205, pp. 103651 (2025) .
https://doi.org/10.1016/j.ijhcs.2025.103651
42. Synthetic Users: user research without the headaches, https://www.syntheticusers.com/
43. Maria Rosala, Kate Moran: Synthetic Users: If, When, and How to Use AI-Generated “Research,” https://www.nngroup.com/articles/synthetic-users/
44. Bender E.M., Gebru T., McMillan-Major A., Shmitchell S.: On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?, Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. pp. 610–623. Association for Computing Machinery, New York, NY, USA (2021) . https://doi.org/10.1145/3442188.3445922
45. Haxvig H.A., Bjørn P., D’Andrea V., Teli M.: Covert DEI Design Techniques for Earthly Survival in Hostile Contexts Proceedings of the 16th Biannual Conference of the Italian SIGCHI Chapter. pp. 1–3. Association for Computing Machinery, New York, NY, USA (2025) . https://doi.org/10.1145/3750069.3755946
46. Haxvig H.A., D’Andrea V., Teli M.: Synthetic Dreams in Barbie Land: Speculative Queer Adventures with Feminist LLM-Generated Personas Companion Publication of the 2025 ACM Designing Interactive Systems Conference. pp. 379–385. ACM, Funchal Portugal (2025) . https://doi.org/10.1145/3715668.3736361
47. Harding S.G.: The science question in feminism, Cornell University Press, Ithaca, (1986)
48. Steiger M., Bharucha T.J., Venkatagiri S., Riedl M.J., Lease M.: The Psychological Well-Being of Content Moderators: The Emotional Labor of Commercial Moderation and Avenues for Improving Support Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. pp. 1–14. Association for Computing Machinery, New York, NY, USA (2021) . https://doi.org/10.1145/3411764.3445092
49. Hicks M.T., Humphries J., Slater J.: ChatGPT is bullshit Ethics Inf. Technol., 26, pp. 38 (2024) . https://doi.org/10.1007/s10676-024-09775-5
50. Bardzell S.: Feminist HCI: taking stock and outlining an agenda for design Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. pp. 1301–1310. Association for Computing Machinery, New York, NY, USA (2010) . https://doi.org/10.1145/1753326.1753521
51. Spiel K., Keyes O., Walker A.M., DeVito M.A., Birnholtz J., Brulé E., Light A., Barlas P., Hardy J., Ahmed A., Rode J.A., Brubaker J.R., Kannabiran G.: Queer(ing) HCI: Moving Forward in Theory and Practice Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. pp. 1–4. Association for Computing Machinery, New York, NY, USA (2019) . https://doi.org/10.1145/3290607.3311750
52. Bratteteig T., Wagner I.: What is a participatory design result? Proceedings of the 14th Participatory Design Conference: Full papers – Volume 1. pp. 141–150. Association for Computing Machinery, New York, NY, USA (2016) .
https://doi.org/10.1145/2940299.2940316
53. Schön D.A.: The reflective practitioner: how professionals think in action, Basic Books, New York, (1983)
54. Bødker S., Christiansen E., Nyvang T., Zander P.-O.: Personas, people and participation: challenges from the trenches of local government Proceedings of the 12th Participatory Design Conference: Research Papers – Volume 1. pp. 91–100. Association for Computing Machinery, New York, NY, USA (2012) . https://doi.org/10.1145/2347635.2347649
55. Bowen J., Petrie H., Hinze A., Samaddar S.: Personas revisited: Extending the Use of Personas to Enhance Participatory Design Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society. pp. 1–12. Association for Computing Machinery, New York, NY, USA (2020) . https://doi.org/10.1145/3419249.3420135
56. Hill C.G., Haag M., Oleson A., Mendez C., Marsden N., Sarma A., Burnett M.: Gender-Inclusiveness Personas vs. Stereotyping: Can We Have it Both Ways? Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. pp. 6658–6671. Association for Computing Machinery, New York, NY, USA (2017) . https://doi.org/10.1145/3025453.3025609
57. Islind A., Lundin J., Cerna K.K., Lindroth T., Åkeflo L., Steineck G.: Proxy design: a method for involving proxy users to speak on behalf of vulnerable or unreachable users in co-design Inf. Technol. People, 38, (2023) . https://doi.org/10.1108/ITP-07-2021-0539
58. Marsden N., Haag M.: Stereotypes and Politics: Reflections on Personas Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. pp. 4017–4031. ACM, San Jose California USA (2016) . https://doi.org/10.1145/2858036.2858151
59. Guridi J.A., Hwang A.H.-C., Santo D., Goula M., Cheyre C., Humphreys L., Rangel M.: From Fake Perfects to Conversational Imperfects: Exploring Image-Generative AI as a Boundary Object for Participatory Design of Public Spaces Proc ACM Hum-Comput Interact, 9, pp. CSCW014:1-CSCW014:33 (2025) . https://doi.org/10.1145/3710912
60. Bardzell S., Bardzell J., Forlizzi J., Zimmerman J., Antanitis J.: Critical design and critical theory: the challenge of designing for provocation Proceedings of the Designing Interactive Systems Conference. pp. 288–297. Association for Computing Machinery, New York, NY, USA (2012) . https://doi.org/10.1145/2317956.2318001
61. Dunne A., Raby F.: Speculative everything: design, fiction, and social dreaming, The MIT Press, Cambridge, Massachusetts ; London, (2013)
62. Lindley J., Sharma D., Potts R.: Anticipatory Ethnography: Design Fiction as an Input to Design Ethnography Ethnogr. Prax. Ind. Conf. Proc., 2014, pp. 237–253 (2014) . https://doi.org/10.1111/1559-8918.01030
63. Turing A.M.: Computing Machinery and Intelligence Mind, 59, pp. 433–460 (1950)
64. Searle J.R.: Minds, brains, and programs Behav. Brain Sci., 3, pp. 417–424 (1980) . https://doi.org/10.1017/S0140525X00005756
65. Fischer G.: A Research Framework Focused on AI and Humans instead of AI versus Humans Interact. Des. Archit., pp. 17–36 (2023) . https://doi.org/10.55612/s-5002-059-001sp