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The Impostor is Among Us: Can Large Language Models Capture the Complexity of Human Personas?

T0 review · 1 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Participants could tell AI-generated personas from human-crafted ones, rating the AI versions as more informative, consistent, and stereotypical.

desk verdict Honest empirical comparison of AI vs human personas, but the one-generator vs ten-author design confound means the main claim is not yet robust; still deserves peer review. read the letter →

arxiv 2501.04543 v2 pith:3HLR42BR submitted 2025-01-08 cs.HC

Christopher Lazik , Christopher Katins , Charlotte Kauter , Jonas Jakob , Caroline Jay , Lars Grunske , Thomas Kosch This is my paper · ORCID
classification cs.HC
keywords personaslargelanguagemodelshuman-computerinteractionAI-generatedcontentstereotypesuserperceptionuser-centereddesignsurveystudy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that lay participants can distinguish human-crafted from AI-generated personas when reading text-only profiles, and that the two kinds are perceived differently on several quality dimensions. AI-generated personas were rated significantly more informative, consistent, clear, and positive, but also more stereotypical, while believability, relatability, and likability did not differ. The study used twenty personas—ten written by HCI researchers who rarely make personas, ten generated by GPT-4o with a zero-shot structured prompt—rated by 54 participants on 7-point Likert scales plus free-text reasoning. The authors conclude that LLMs can produce personas that hit many surface quality marks yet lean on stereotypes and a robotic writing style, a risk for novices who might take them at face value. This finding runs contrary to an earlier study that found AI and human personas indistinguishable.

What carries the argument

The central machinery is a set of perception constructs from the persona perception scale and related prior work—informativeness, believability, stereotypicality, positivity, relatability, consistency, clarity, and likability—each measured with 7-point Likert statements, plus two origin-judgment statements. The study design compares two sets of ten text-only personas: human-crafted personas written by ten HCI researchers following a template based on GenderMag and prior persona research, and AI-generated personas created with GPT-4o using a two-stage zero-shot prompt that first builds skeletal personas and then expands them into narratives. Ratings from 54 participants were averaged per participant for each persona type and compared with Wilcoxon signed-rank tests, while free-text responses were analyzed with inductive coding to identify the features that drive discrimination.

What would settle it

Run the same survey with personas crafted by professional UX designers based on real user interviews, keeping the same generation model, persona template, and participant pool; if participants can no longer distinguish the two sets or rule out stereotypicality differences, the paper's central claim would be refuted.

Watch

Extended reading notes

Core claim

On the paper's own terms, participants could tell the difference between the two types of personas: human-crafted personas received higher ratings on the statement that a persona is human-crafted, and AI-generated personas received higher ratings on the statement that a persona is AI-generated. Beyond origin judgments, AI personas were rated as more informative for design, more consistent, clearer, more positive, and more stereotypical, while the two sets did not differ significantly in believability, relatability, or likability. Qualitative analysis of free-text explanations shows that participants relied on writing style, the presence of emotional or personal detail, stereotypicality, perceived realism, and the balance of positive and negative depiction. The overall conclusion is that LLMs can generate personas that meet many quality aspects but also include stereotypical characterizations, which threatens the diversity of requirements if such personas are used uncritically.

Load-bearing premise

The human baseline personas were written by ten HCI researchers who mostly had never created a persona before, so the 'human-crafted' set is closer to knowledgeable novices than to professional persona designers, and the findings may not generalize to personas crafted by experts after real user interviews.

Editorial extensions

If this is right

  • Novices who rely on AI-generated personas may be misled by their clarity and consistency while unknowingly adopting stereotypical characterizations.
  • Practitioners should verify AI-generated personas for diversity and bias before using them in design, because stereotypicality was a notable feature of the generated set.
  • The detectable writing style of LLM personas—described as robotic and full of unnecessary details—suggests that prompt engineering or few-shot examples could alter how human-like these personas appear.
  • The result contradicts the earlier finding that AI and human personas are indistinguishable, implying that the question of detectability depends on the human baseline and the evaluation method.
  • AI-generated personas may be best used as a starting point or supplement to, rather than a replacement for, personas grounded in real user research.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The detectability result may be tied to the specific human baseline: professional persona designers working from real user interviews might produce personas that are harder to distinguish from AI output, so the paper's claim is limited to novice-like human crafting.
  • The stereotypicality finding suggests a concrete testable extension: prompting the LLM with explicit diversity guidelines or injecting real user data before generation could reduce stereotypical content, and a follow-up survey could measure whether participants then rate the personas as less stereotypical.
  • The same perceptual gap might appear in other AI-generated user representations, such as synthetic users in usability testing or AI-written user stories, where surface polish could mask a lack of contextual and emotional depth.
  • The robotic tone identified by participants indicates that style-based detection cues are present in the current generation, but these cues may be fragile if future models adopt more conversational writing styles.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

1 major / 5 minor

Summary. This paper reports a mixed-methods user study comparing ten personas written by HCI-aware participants with ten personas generated by GPT-4o using a zero-shot structured prompt. Fifty-four Prolific participants rated all twenty personas on 7-point Likert scales for origin and eight quality dimensions, and provided free-text justifications. The authors report that participants rated human-crafted personas as more human-crafted and AI personas as more AI-generated, that AI personas were rated as more informative, positive, consistent, clear, and stereotypical, and that no significant differences were found for believability, relatability, or likability. Qualitative analysis identified writing style, information content, stereotypicality, realism and appeal, and positive versus negative tone as cues used by participants. The paper concludes that LLM-generated personas can meet many quality aspects but tend toward stereotypical depictions.

Significance. If the findings held as stated, the paper would make a useful contribution to the HCI literature on LLM-generated personas, particularly by documenting perceptual cues and the risk of stereotyping. The study's strengths include the use of newly collected human personas to avoid training-data leakage, the systematic use of a published prompting strategy, and the combination of quantitative ratings with thematic analysis. Effect sizes are reported, and the qualitative excerpts ground the proposed mechanisms in concrete participant reasoning. However, the headline conclusion is currently supported only at the level of stimulus sets rather than individual personas, and several secondary results rest on uncorrected multiple tests; the significance of the paper therefore depends on repair of these issues.

major comments (1)
  1. [§5.1 and §4.4] The experiment confounds stimulus origin with the number of generators: the ten AI personas were produced by a single model (GPT-4o) with a single zero-shot prompt (Section 5.1), whereas the ten human personas were written by ten different individuals (Section 4.4). Because every participant saw all twenty personas (Section 5.4), ratings of 'AI-generated' could have been based on the shared stylistic and structural uniformity of the one generator rather than on cues present in an individual AI-generated persona. The qualitative data show that participants did exploit this set-level cue, for example P15's statement about 'a striking resemblance in the words and phrases used' (Section 5.8.1). This undermines the RQ1 conclusion in Section 6.1 that participants could distinguish between human-crafted and AI-generated personas at the level of individual personas, and it also threatens the consistency (Section 5.7.7) and clarity (Section 5.7.8) findings, which may simply reflect the template-like output of a single generation pipeline. To support the central claim, the authors would need to vary generators or prompts, or to analyze the data in a way that controls for this homogeneity.
minor comments (5)
  1. [Fig. 6a / §5.7.6] Figure 6a's caption states that participants rated the relatability of AI-generated personas higher than that of human-crafted personas, but Section 5.7.6 reports no significant difference in relatability; the caption should be corrected.
  2. [§5.7.6] Section 5.7.6 contains a copy-and-paste error: it says the test did not indicate a significant difference in believability, but the statement under test is relatability.
  3. [§5.8] Section 5.8 reports an inductive coding process but does not provide inter-coder reliability (e.g., Cohen's kappa or Krippendorff's alpha) for the qualitative theme coding; this should be added for transparency.
  4. [Table 3 / §6.1] The two origin questions in Table 3 are not forced-choice; reporting classification accuracy (e.g., the proportion of personas correctly labeled under a decision rule) in addition to the Likert means would make the 'distinguish' claim in Section 6.1 easier to interpret.
  5. [§5.7] Most dimensions (e.g., informativeness, believability, stereotypicality) are measured with single items and then treated as scales; the authors should note this psychometric limitation relative to the multi-item persona perception scale.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the central empirical claim is measured directly from participant ratings, with no fitted parameters or self-citation chain.

full rationale

This paper is an empirical perception study rather than a derivation. The central claim, that participants can distinguish human-crafted from AI-generated personas, is supported by Likert-scale ratings collected from 54 independent participants on 20 randomized personas. There is no predicted quantity derived from the stimulus set, no parameter fitted to the outcome, and no mathematical or definitional chain that would make the conclusion equivalent to the inputs. The AI personas were generated with a published zero-shot prompting strategy from Salminen et al. [39], and the evaluation used constructs from the persona perception scale [41]; these are external prior works with no author overlap with the present paper, so reusing their instruments is not a self-citation chain. The two papers by the present authors that are cited ([21] Kosch and Feger, [22] Krauss et al.) appear only in the motivation about risks of LLMs and are not load-bearing for the empirical results. The paper's acknowledged limitations, such as the novice-like human baseline and the small persona set, weaken generalizability but do not reduce any outcome to the inputs by construction. The single-generator versus multi-author design confound is a validity threat, not circularity, because the observed distinction is not logically entailed by the setup. Therefore no significant circularity is present.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The study does not fit free parameters; it makes domain assumptions about the representativeness of its persona samples, the validity of the perception constructs, and the choice of GPT-4o and prompt strategy as a baseline for LLM persona generation.

assumptions (4)
  • domain assumption The persona perception scale constructs are valid measures of persona perception in this context.
    The study relies on these constructs from Salminen et al. [39,41] without revalidating them for this comparison.
  • domain assumption Likert ratings averaged across personas for each participant can be compared with Wilcoxon signed-rank tests to characterize differences between persona types.
    Section 5.7 averages item ratings across personas per participant; this assumes no meaningful persona-level variation is lost.
  • domain assumption The ten HCI experts' personas represent a valid baseline of human-crafted personas despite the authors' admission that these experts are not frequent persona creators and are 'less far from novices'.
    Sections 4 and 6.4 acknowledge this limits generalizability to expert-crafted personas.
  • domain assumption OpenAI's GPT-4o with the two-step zero-shot prompt (skeleton then expansion) is a representative example of LLM persona generation.
    Section 5.1; the authors note this is a baseline scenario and that other prompt strategies might yield different results (Section 6.5).

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Cite this review

Pith. "Pith review of The Impostor is Among Us: Can Large Language Models Capture the Complexity of Human Personas?." pith.science (2026). https://pith.science/paper/3HLR42BR

@misc{pith2026250104543,
  author       = {Pith},
  title        = {Pith review of: The Impostor is Among Us: Can Large Language Models Capture the Complexity of Human Personas?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3HLR42BR}},
  note         = {Machine review of arXiv:2501.04543}
}
read the original abstract

Large Language Models (LLMs) created new opportunities for generating personas, expected to streamline and accelerate the human-centered design process. Yet, AI-generated personas may not accurately represent actual user experiences, as they can miss contextual and emotional insights critical to understanding real users' needs and behaviors. This introduces a potential threat to quality, especially for novices. This paper examines the differences in how users perceive personas created by LLMs compared to those crafted by humans regarding their credibility for design. We gathered ten human-crafted personas developed by HCI experts according to relevant attributes established in related work. Then, we systematically generated ten personas with an LLM and compared them with human-crafted ones in a survey. The results showed that participants differentiated between human-created and AI-generated personas, with the latter perceived as more informative and consistent. However, participants noted that the AI-generated personas tended to follow stereotypes, highlighting the need for a greater emphasis on diversity when utilizing LLMs for persona creation.

Figures

Figures reproduced from arXiv: 2501.04543 by the authors.

Figure 1
Figure 1. We investigated if and how users discern between textual descriptions of human-crafted and AI-generated personas. In [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Two example personas from our survey study. [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Violin plot comparing if participants could distinguish between human-crafted and AI-generated personas. [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Violin plot comparing how participants rated the informativeness and the realism of human-crafted and AI-generated personas. [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: Violin plot comparing how participants rated the stereotypicality and the positivity of human-crafted and AI-generated [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 6
Figure 6. Figure 6: Violin plot comparing how participants rated the relatability and the consistency of human-crafted and AI-generated personas. [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 7
Figure 7. Figure 7: Violin plot comparing how participants rated the clarity and the likability of human-crafted and AI-generated personas. [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]

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Cited by 1 Pith paper

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Reference graph

Works this paper leans on

49 extracted references · 19 canonical work pages · cited by 1 Pith paper

  1. [1]

    Tamara Adlin and John Pruitt. 2010. The Essential Persona Lifecycle: Your Guide to Building and Using Personas . Morgan Kaufmann. Google-Books-ID: fvmN0Fr5c_MC

    work page 2010

  2. [2]

    Stevie Bergman, Jennifer Chien, Mark Díaz, Seliem El-Sayed, Jaylen Pittman, Shakir Mohamed, and Kevin R

    William Agnew, A. Stevie Bergman, Jennifer Chien, Mark Díaz, Seliem El-Sayed, Jaylen Pittman, Shakir Mohamed, and Kevin R. McKee. 2024. The Illusion of Artificial Inclusion. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24) . Association for Computing Machinery, New York, NY, USA, 1–12. doi:10.1145/3613904.3642703

    arXiv 2024

  3. [3]

    Arriaga, and Adam Tauman Kalai

    Gati V Aher, Rosa I. Arriaga, and Adam Tauman Kalai. 2023. Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies. In Proceedings of the 40th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 202) , Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabat...

    work page 2023

  4. [4]

    Khaled Albusays, Pernille Bjorn, Laura Dabbish, Denae Ford, Emerson Murphy-Hill, Alexander Serebrenik, and Margaret-Anne Storey. 2021. The Diversity Crisis in Software Development. IEEE Software 38, 2 (2021), 19–25. doi:10.1109/MS.2020.3045817

    arXiv 2021

  5. [5]

    Argyle, Ethan C

    Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, Joshua R. Gubler, Christopher Rytting, and David Wingate. 2023. Out of One, Many: Using Language Models to Simulate Human Samples. Political Analysis 31, 3 (2023), 337–351. doi:10.1017/pan.2023.2

    doi:10.1017/pan.2023.2 2023

  6. [6]

    Ann Blandford, Dominic Furniss, and Stephann Makri. 2016. Qualitative HCI research: Going behind the scenes . Morgan & Claypool Publishers

    work page 2016

  7. [7]

    Margaret Burnett, Simone Stumpf, Jamie Macbeth, Stephann Makri, Laura Beckwith, Irwin Kwan, Anicia Peters, and William Jernigan. 2016. GenderMag: A Method for Evaluating Software’s Gender Inclusiveness.Interacting with Computers 28, 6 (Nov. 2016), 760–787. doi:10.1093/iwc/iwv046

    doi:10.1093/iwc/iwv046 2016

  8. [8]

    Courtni Byun, Piper Vasicek, and Kevin Seppi. 2023. Dispensing with Humans in Human-Computer Interaction Research. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA ’23) . Association for Computing Machinery, New York, NY, USA. doi:10.1145/3544549.3582749

    arXiv 2023

Show all 49 references
  1. [9]

    Yen-ning Chang, Youn-kyung Lim, and Erik Stolterman. 2008. Personas: from theory to practices. In Proceedings of the 5th Nordic conference on Human-computer interaction: building bridges (NordiCHI ’08) . Association for Computing Machinery, New York, NY, USA, 439–442. doi:10.1...

    2008

  2. [10]

    Cheng-Han Chiang and Hung-yi Lee. 2023. Can Large Language Models Be an Alternative to Human Evaluations? _eprint: 2305.01937

    2023 arXiv

  3. [11]

    Alan Cooper. 1999. The Inmates are Running the Asylum . Vieweg+Teubner Verlag, Wiesbaden, 17–17. doi:10.1007/978-3-322-99786-9_1

    1999 doi

  4. [12]

    Danica Dillion, Niket Tandon, Yuling Gu, and Kurt Gray. 2023. Can AI language models replace human participants? Trends in Cognitive Sciences (2023). Publisher: Elsevier. Manuscript submitted to ACM 26 Lazik et al

    2023

  5. [13]

    Marco Gerosa, Bianca Trinkenreich, Igor Steinmacher, and Anita Sarma. 2024. Can AI serve as a substitute for human subjects in software engineering research? Automated Software Engineering 31, 1 (2024), 13. doi:10.1007/s10515-023-00409-6 Publisher: Springer

    2024 doi

  6. [14]

    Fabrizio Gilardi, Meysam Alizadeh, and Maël Kubli. 2023. ChatGPT outperforms crowd workers for text-annotation tasks.Proceedings of the National Academy of Sciences 120, 30 (2023), e2305016120. doi:10.1073/pnas.2305016120 _eprint: https://www.pnas.org/doi/pdf/10.1073/pnas.2305016120

    2023 doi

  7. [15]

    Tom Heyman and Geert Heyman. 2023. The impact of ChatGPT on human data collection: A case study involving typicality norming data. Behavior Research Methods (2023), 1–8. doi:10.3758/s13428-023-02235-w Publisher: Springer

    2023 doi

  8. [16]

    John J Horton. 2023. Large language models as simulated economic agents: What can we learn from homo silicus? Technical Report. National Bureau of Economic Research. doi:10.3386/w31122

    2023 doi

  9. [17]

    Perttu Hämäläinen, Mikke Tavast, and Anton Kunnari. 2023. Evaluating Large Language Models in Generating Synthetic HCI Research Data: a Case Study. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23) . Association for Computing Machinery, ...

    2023

  10. [18]

    Guan, and Joni Salminen

    Bernard Jansen, Soon-Gyo Jung, Lene Nielsen, Kathleen W. Guan, and Joni Salminen. 2022. How to Create Personas: Three Persona Creation Methodologies with Implications for Practical Employment. Pacific Asia Journal of the Association for Information Systems 14, 3 (March 2022). ...

    2022 doi

  11. [19]

    Jansen, Joni Salminen, Soon-gyo Jung, and Kathleen Guan

    Bernard J. Jansen, Joni Salminen, Soon-gyo Jung, and Kathleen Guan. 2021. Creating Data-Driven Personas. Springer International Publishing, Cham, 93–118. doi:10.1007/978-3-031-02231-9_4

    2021 doi

  12. [20]

    Soon-Gyo Jung, Joni Salminen, Kholoud Khalil Aldous, and Bernard J. Jansen. 2025. PersonaCraft: Leveraging language models for data-driven persona development. International Journal of Human-Computer Studies 197 (2025), 103445. doi:10.1016/j.ijhcs.2025.103445

    2025

  13. [21]

    Thomas Kosch and Sebastian Feger. 2024. Risk or Chance? Large Language Models and Reproducibility in Human-Computer Interaction Research. doi:10.48550/arXiv.2404.15782 arXiv:2404.15782 [cs]

    2024 doi arXiv

  14. [22]

    Create a Fear of Missing Out

    Veronika Krauß, Mark McGill, Thomas Kosch, Yolanda Thiel, Dominik Schön, and Jan Gugenheimer. 2024. "Create a Fear of Missing Out" – ChatGPT Implements Unsolicited Deceptive Designs in Generated Websites Without Warning. arXiv:2411.03108 [cs.HC] https://arxiv.org/abs/2411.03108

    2024 arXiv

  15. [23]

    Scale for the assessment of non- experts’ AI literacy

    Matthias Carl Laupichler, Alexandra Aster, Nicolas Haverkamp, and Tobias Raupach. 2023. Development of the “Scale for the assessment of non- experts’ AI literacy” – An exploratory factor analysis. Computers in Human Behavior Reports 12 (Dec. 2023), 100338. doi:10.1016/j.chbr.2...

    2023

  16. [24]

    Tara Matthews, Tejinder Judge, and Steve Whittaker. 2012. How do designers and user experience professionals actually perceive and use personas?. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Austin, Texas, USA)(CHI ’12). Association for Comput...

    2012

  17. [25]

    Tomasz Miaskiewicz and Kenneth A. Kozar. 2011. Personas and user-centered design: How can personas benefit product design processes? Design Studies 32, 5 (Sept. 2011), 417–430. doi:10.1016/j.destud.2011.03.003

    2011 doi

  18. [26]

    Timothy Neate, Aikaterini Bourazeri, Abi Roper, Simone Stumpf, and Stephanie Wilson. 2019. Co-Created Personas: Engaging and Empowering Users with Diverse Needs Within the Design Process. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow,...

    2019

  19. [27]

    Nickerson

    Raymond S. Nickerson. 1998. Confirmation Bias: A Ubiquitous Phenomenon in Many Guises. Review of General Psychology 2, 2 (1998), 175–220. doi:10.1037/1089-2680.2.2.175 arXiv:https://doi.org/10.1037/1089-2680.2.2.175

    1998 doi

  20. [28]

    Lene Nielsen. 2013. Personas-user focused design. Vol. 15. Springer

    2013

  21. [29]

    Lene Nielsen. 2019. Persona Writing. Springer London, London, 55–81. doi:10.1007/978-1-4471-7427-1_4

    2019 doi

  22. [30]

    Lene Nielsen, Kira Storgaard Hansen, Jan Stage, and Jane Billestrup. 2015. A template for design personas: analysis of 47 persona descriptions from danish industries and organizations. International Journal of Sociotechnology and Knowledge Development (IJSKD) 7, 1 (2015), 45–61

    2015

  23. [31]

    Bernstein

    Joon Sung Park, Lindsay Popowski, Carrie Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. 2022. Social Simulacra: Creating Populated Prototypes for Social Computing Systems. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Techn...

    2022

  24. [32]

    John Pruitt and Jonathan Grudin. 2003. Personas: practice and theory. In Proceedings of the 2003 Conference on Designing for User Experiences (San Francisco, California) (DUX ’03). Association for Computing Machinery, New York, NY, USA, 1–15. doi:10.1145/997078.997089

    2003

  25. [33]

    Kari Rönkkö, Mats Hellman, Britta Kilander, and Yvonne Dittrich. 2004. Personas is not applicable: local remedies interpreted in a wider context. In Proceedings of the Eighth Conference on Participatory Design: Artful Integration: Interweaving Media, Materials and Practices - ...

    2004

  26. [34]

    Chowdhury, and Bernard J

    Joni Salminen, Kathleen Guan, Soon-Gyo Jung, Shammur A. Chowdhury, and Bernard J. Jansen. 2020. A Literature Review of Quantitative Persona Creation. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for ...

    2020

  27. [35]

    Joni Salminen, Bernard J Jansen, Jisun An, Haewoon Kwak, and Soon-gyo Jung. 2018. Are personas done? Evaluating their usefulness in the age of digital analytics. Persona Studies 4, 2 (2018), 47–65. doi:10.21153/psj2018vol4no2art737

    2018 doi

  28. [36]

    Jansen, Jisun An, Haewoon Kwak, and Soon-Gyo Jung

    Joni Salminen, Bernard J. Jansen, Jisun An, Haewoon Kwak, and Soon-Gyo Jung. 2019. Automatic Persona Generation for Online Content Creators: Conceptual Rationale and a Research Agenda . Springer London, London, 135–160. doi:10.1007/978-1-4471-7427-1_8

    2019 doi

  29. [37]

    Joni Salminen, Soon-gyo Jung, and Bernard J Jansen. 2019. The Future of Data-driven Personas: A Marriage of Online Analytics Numbers and Human Attributes.. In ICEIS (1). 608–615. Manuscript submitted to ACM The Impostor is Among Us 27

    2019

  30. [38]

    Joni Salminen, Soon-Gyo Jung, Lene Nielsen, and Bernard Jansen. 2022. Creating More Personas Improves Representation of Demographically Diverse Populations: Implications Towards Interactive Persona Systems. In Nordic Human-Computer Interaction Conference (NordiCHI ’22) . Assoc...

    2022

  31. [39]

    Joni Salminen, Chang Liu, Wenjing Pian, Jianxing Chi, Essi Häyhänen, and Bernard J Jansen. 2024. Deus Ex Machina and Personas from Large Language Models: Investigating the Composition of AI-Generated Persona Descriptions. In Proceedings of the CHI Conference on Human Factors i...

    2024

  32. [40]

    Is More Better?

    Joni Salminen, Lene Nielsen, Soon-Gyo Jung, Jisun An, Haewoon Kwak, and Bernard J. Jansen. 2018. “Is More Better?”: Impact of Multiple Photos on Perception of Persona Profiles. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada...

    2018

  33. [41]

    Santos, Haewoon Kwak, Jisun An, Soon gyo Jung, and Bernard J

    Joni Salminen, Joao M. Santos, Haewoon Kwak, Jisun An, Soon gyo Jung, and Bernard J. Jansen. 2020. Persona Perception Scale: Development and Exploratory Validation of an Instrument for Evaluating Individuals’ Perceptions of Personas. International Journal of Human-Computer Stu...

    2020

  34. [42]

    Joni Salminen, Kathleen Wenyun Guan, Soon-Gyo Jung, and Bernard Jansen. 2022. Use Cases for Design Personas: A Systematic Review and New Frontiers. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22) . Association for Computing Machinery, ...

    2022

  35. [43]

    Albrecht Schmidt, Passant Elagroudy, Fiona Draxler, Frauke Kreuter, and Robin Welsch. 2024. Simulating the Human in HCD with ChatGPT: Redesigning Interaction Design with AI. interactions 31, 1 (Jan. 2024), 24–31. doi:10.1145/3637436

    2024 doi

  36. [44]

    Sander Schulhoff, Jeremy Pinto, Anaum Khan, Louis-François Bouchard, Chenglei Si, Svetlina Anati, Valen Tagliabue, Anson Kost, Christopher Carnahan, and Jordan Boyd-Graber. 2023. Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs Through a Global Prom...

    2023

  37. [45]

    Andreas Schuller, Doris Janssen, Julian Blumenröther, Theresa Maria Probst, Michael Schmidt, and Chandan Kumar. 2024. Generating personas using LLMs and assessing their viability. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems . ACM, Honolulu...

    2024

  38. [46]

    Hedderich, Bartłomiej Jakub Rey, Andrés Lucero, and Antti Oulasvirta

    Joongi Shin, Michael A. Hedderich, Bartłomiej Jakub Rey, Andrés Lucero, and Antti Oulasvirta. 2024. Understanding Human-AI Workflows for Generating Personas. In Proceedings of the 2024 ACM Designing Interactive Systems Conference (Copenhagen, Denmark) (DIS ’24). Association fo...

    2024

  39. [47]

    Wilbert Tabone and Joost De Winter. 2023. Using ChatGPT for human–computer interaction research: a primer. Royal Society Open Science 10, 9 (Sept. 2023), 231053. doi:10.1098/rsos.231053

    2023 doi

  40. [48]

    Volker Thoma and Bryn Williams. 2009. Developing and Validating Personas in e-Commerce: A Heuristic Approach. InHuman-Computer Interaction – INTERACT 2009, Tom Gross, Jan Gulliksen, Paula Kotzé, Lars Oestreicher, Philippe Palanque, Raquel Oliveira Prates, and Marco Winckler (E...

    2009

  41. [49]

    Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu, and Michael Zeng. 2021. Want To Reduce Labeling Cost? GPT-3 Can Help. _eprint: 2108.13487. Manuscript submitted to ACM

    2021 arXiv

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.