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arxiv: 2504.04927 · v2 · submitted 2025-04-07 · 💻 cs.HC · cs.CL

Creating and Evaluating Personas Using Generative AI: A Scoping Review of 81 Articles

Danial Amin , Joni Salminen , Farhan Ahmed , Sonja M.H. Tervola , Sankalp Sethi , Bernard J. Jansen This is my paper

Pith reviewed 2026-05-22 20:51 UTC · model grok-4.3

classification 💻 cs.HC cs.CL
keywords generative AIpersonasscoping reviewevaluation methodsuser modelingreproducibilitycircular evaluationhuman-AI collaboration
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The pith

A review of 81 studies finds generative AI for user personas often skips evaluation and risks the same model generating and judging its own outputs.

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

This paper examines recent practices for building personas with generative AI by looking across 81 articles published between 2022 and 2025. It tracks how these AI-created stand-ins for users are produced, tested, and shared, while noting patterns such as heavy dependence on one model family and the spread of chat-style persona interfaces. The work draws attention to cases where nearly half the studies perform no checks on the personas and where the AI may end up assessing what it created, which could weaken the reliability of the results. It also records that human developers appear to play a smaller part once generative models enter the workflow. These observations matter because personas shape design choices and user research, so weaknesses in their construction can carry forward into products and services.

Core claim

The scoping review finds that 61 percent of the articles share resources such as personas, code, or datasets, supporting reproducibility, while conversational persona interfaces appear more often alongside traditional profile formats. At the same time, 45 percent of articles include no evaluation step and 86 percent rely exclusively on GPT models. In several cases the same generative model both produces and assesses the personas, creating a circularity risk, and overall the approach appears to shrink the direct contribution of human developers during creation. The authors respond by outlining practical guidelines to support responsible use of the technology in persona work.

What carries the argument

Scoping review that sorts articles by generative AI application, evaluation presence, model choice, resource sharing, and interface style to surface patterns in persona development.

If this is right

  • Persona studies that omit evaluation steps may deliver user representations whose fit to actual people remains unknown.
  • Dominant use of a single model family limits the variety of outputs and may embed shared biases across many projects.
  • Circular generation and evaluation loops can produce self-reinforcing results that appear consistent without external confirmation.
  • Reduced human involvement shifts more responsibility for accuracy and relevance onto the generative model itself.
  • Proposed guidelines offer a concrete route to restore checks and broaden model choices in future persona work.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Teams adopting these tools may need new quality benchmarks that avoid depending on the generating model for validation.
  • Wider industry use could amplify any model-specific blind spots in how user groups are portrayed over time.
  • The move toward conversational personas might require fresh testing methods focused on dialogue quality rather than static profiles.
  • Maintaining some human checkpoints at key stages could be tested as a practical safeguard against over-reliance on generative outputs.

Load-bearing premise

The 81 articles located by the search strategy form a representative sample of current generative AI practices for personas, and the extraction accurately reflects evaluation methods, model use, and reproducibility without major selection or reading bias.

What would settle it

A follow-up search that turns up many additional articles using non-GPT models together with independent human evaluations of the generated personas would test whether the reported patterns hold.

Figures

Figures reproduced from arXiv: 2504.04927 by Bernard J. Jansen, Danial Amin, Farhan Ahmed, Joni Salminen, Sankalp Sethi, Sonja M.H. Tervola.

Figure 1
Figure 1. Figure 1: The initial database search yielded 573 articles: 37 from WoS, 195 from Scopus, 31 from [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 1
Figure 1. Figure 1: PRISMA Flow Diagram describing the literature collection process. [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Representation of the current state of GenAI use in persona development. [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Representation of different methodological concepts in AI-generated personas. [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Current research in AI-generated personas. [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Use cases and contexts in AI-generated personas. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Use of LLMs in persona evaluation. exposure of designers to potentially harmful content during the evaluation process [78]. Further￾more, AI-generated personas could lead to prioritizing narratives that contribute to discrimination [22] and potentially exacerbate polarization on challenging topics such as climate change [68] and politics [59]. Third, there are trust and validity concerns about the authenti… view at source ↗
Figure 7
Figure 7. Figure 7: Ethical considerations, limitations, and future research in AI-generated personas. [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Use cases for Human-AI collaboration in AI-generated personas. [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Reproducability of AI-generated personas [PITH_FULL_IMAGE:figures/full_fig_p025_9.png] view at source ↗
read the original abstract

As generative AI (GenAI) is increasingly applied in persona development to represent real users, understanding the implications and limitations of this technology is essential for establishing robust practices. This scoping review analyzes how 81 articles (2022-2025) use GenAI techniques for the creation, evaluation, and application of personas. The articles exhibited good level of reproducibility, with 61% of articles sharing resources (personas, code, or datasets). Furthermore, conversational persona interfaces are increasingly provided alongside traditional profiles. However, nearly half (45%) of the articles lack evaluation, and the majority (86%) use only GPT models. In some articles, GenAI use creates a risk of circularity, in which the same GenAI model both generates and evaluates outputs. Our findings also suggest that GenAI seems to reduce the role of human developers in the persona-creation process. To mitigate the associated risks, we propose actionable guidelines for the responsible integration of GenAI into persona development.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript presents a scoping review of 81 articles (2022-2025) examining the use of generative AI for persona creation, evaluation, and application. It reports that 61% of articles share resources, 45% lack evaluation, 86% rely exclusively on GPT models, identifies circularity risks where the same GenAI model generates and evaluates outputs, observes a reduction in human developer roles, and proposes guidelines for responsible integration.

Significance. If the sample is representative and extraction reliable, the review provides a timely synthesis of GenAI practices in persona development, with credit due for the scale of the review (81 articles), explicit quantification of reproducibility and evaluation gaps, and the identification of circularity as a distinct risk. These elements offer a useful reference point for HCI researchers adopting GenAI tools.

major comments (2)
  1. [Methods] Methods section: The description of the literature search lacks the exact search strings, list of databases, PRISMA flow diagram, and any inter-rater reliability metrics for article screening and data extraction. This directly affects confidence in the representativeness of the 81-article sample and the reliability of derived statistics (86% GPT-only, 45% no evaluation, circularity observations).
  2. [Results] Results section (circularity and human-role findings): The claims that GenAI creates circularity risks and reduces human developer involvement rest on interpretive categorization of the reviewed articles without a reported coding scheme or agreement statistics; this is load-bearing for the risk-assessment conclusions and the proposed guidelines.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'good level of reproducibility' should be replaced with the specific figure (61%) for precision.
  2. [Tables/Figures] Tables/figures: Any summary tables of article characteristics would benefit from explicit column definitions for categories such as 'evaluation presence' to avoid ambiguity in interpretation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our scoping review. The feedback highlights important areas for improving transparency in methods and supporting interpretive claims in results. We address each major comment below and indicate planned revisions.

read point-by-point responses

  1. Referee: [Methods] Methods section: The description of the literature search lacks the exact search strings, list of databases, PRISMA flow diagram, and any inter-rater reliability metrics for article screening and data extraction. This directly affects confidence in the representativeness of the 81-article sample and the reliability of derived statistics (86% GPT-only, 45% no evaluation, circularity observations).

    Authors: We agree that additional methodological detail is needed to strengthen confidence in the sample and statistics. In the revised manuscript we will add the exact search strings, the complete list of databases, and a PRISMA flow diagram. The screening and extraction process was led by one author with team oversight rather than independent dual coding; therefore formal inter-rater reliability metrics are not available. We will describe the exact procedure and any quality-control steps taken so readers can evaluate reliability. revision: partial

  2. Referee: [Results] Results section (circularity and human-role findings): The claims that GenAI creates circularity risks and reduces human developer involvement rest on interpretive categorization of the reviewed articles without a reported coding scheme or agreement statistics; this is load-bearing for the risk-assessment conclusions and the proposed guidelines.

    Authors: The circularity and human-role observations were derived from systematic reading of the 81 articles. To make the basis for these claims transparent, we will add a dedicated subsection in the methods (or an appendix) that details the categorization criteria, coding scheme, and decision rules used to identify circularity and changes in human involvement. This will allow readers to assess the interpretive steps supporting the risk discussion and guidelines. revision: yes

Circularity Check

0 steps flagged

No circularity in scoping review synthesis

full rationale

This paper performs a scoping review of 81 articles on GenAI for persona creation and evaluation. Its claims consist of observational tallies (e.g., 86% GPT-only usage, 45% lacking evaluation, occasional same-model generate-and-evaluate loops in reviewed works) drawn from the sampled literature rather than any derivation, equation, parameter fit, or prediction that reduces to the paper's own inputs by construction. No self-definitional loops, fitted-input predictions, or load-bearing self-citation chains appear; the synthesis is self-contained against the external corpus it reviews.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

As a scoping review the paper rests on standard assumptions about literature search completeness and consistent interpretation of 'evaluation' and 'circularity' across heterogeneous articles; no free parameters or invented entities are introduced.

axioms (2)
  • domain assumption The search strategy and inclusion criteria applied to identify the 81 articles capture the relevant population of GenAI persona studies without material omission or bias.
    Invoked implicitly in the scoping review methodology to support the reported proportions and trends.
  • domain assumption Evaluation absence, model usage, and resource sharing can be reliably extracted and categorized from article texts using consistent definitions.
    Underpins the 45%, 86%, and 61% statistics presented as key findings.

pith-pipeline@v0.9.0 · 5721 in / 1460 out tokens · 29233 ms · 2026-05-22T20:51:46.865584+00:00 · methodology

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