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The role of large language models in UI/UX design: A systematic literature review

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The first systematic review dedicated to large language models in UI/UX design claims LLMs now act as collaborators across the entire design lifecycle.

desk verdict A transparent, useful first LLM-specific UI/UX review whose frequency counts rest on a shaky sampling shortcut; worth refereeing with a sensitivity check required. read the letter →

arxiv 2507.04469 v2 pith:NFY3B6ON submitted 2025-07-06 cs.HC cs.AIcs.CL

Ammar Ahmed , Ali Shariq Imran This is my paper
classification cs.HCcs.AIcs.CL
keywords LargeLanguageModels(LLMs)UI/UXDesignHuman-AICollaborationPromptEngineeringGenerativeAIinsystematicliteraturereviewlifecycleGPT-4
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

This paper claims to be the first systematic literature review devoted specifically to how large language models are used in UI/UX design workflows. Analyzing 38 peer-reviewed studies from 2022 to 2025, it aims to establish that LLMs, led by GPT-4 and its multimodal variants, are now used across the full design lifecycle, from ideation and prototyping to evaluation and refinement. The review also tries to consolidate emerging best practices and persistent challenges, arguing that LLMs are becoming collaborators in design rather than mere automation tools. If the synthesis is accurate, it gives practitioners and researchers a shared map of a fast-moving field.

What carries the argument

The machinery is the systematic review protocol itself: a three-database Boolean search across ACM, IEEE Xplore, and Scopus for 2022 to 2025, a relevance-based cutoff to keep screening tractable, exclusion and inclusion criteria that reduce 2,668 initial hits to 38 studies, and structured data extraction across nine fields. The analytical backbone is a mapping of LLM integration onto the stages of the UI/UX design lifecycle, including research and discovery, ideation, design generation, prototyping and simulation, evaluation and feedback, iterative refinement, and reflection and ethics, which turns scattered case studies into a coherent claim about full-lifecycle adoption.

What would settle it

Re-run the search across ACM, IEEE Xplore, and Scopus without the relevance-based cutoff, or at least screen the pages beyond the cutoffs, and check whether additional UI/UX and LLM studies surface; if new studies change the corpus, the claims about GPT-4 dominance, the 2024 peak, and the best-practice clusters would need revision.

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Extended reading notes

Core claim

The authors set out to answer three research questions: which open-source and proprietary LLMs are used in UI/UX work, how they are integrated, and what best practices and limitations the literature reports. Their central finding is that LLM integration is already full-spectrum: GPT-4 appears in 18 of the 38 reviewed studies, with GPT-3.5, PaLM, Gemini, GPT-4V, and others playing supporting or comparison roles, and the models are embedded as plugins inside design tools, driven by prompts as a design language, and applied from research and discovery through evaluation and iterative refinement. The review further claims that the most effective use is human-in-the-loop and modular, that structured prompting, tool integration, and multimodal inputs are the emerging best practices, and that hallucinations, prompt instability, limited explainability, and ethical or privacy concerns are the recurring obstacles. The authors conclude that the integration landscape is still fragmented, lacking shared standards, and call for validation mechanisms, prompt-design support, ethical safeguards, and evaluation benchmarks.

Load-bearing premise

The load-bearing premise is that stopping the database search once a results page drops below five relevant titles out of ten does not miss important studies buried deeper in the rankings; if relevant work sits beyond the cutoff, the count of 38 studies and the trends built on it are artifacts of the sampled subset.

Editorial extensions

If this is right

  • If LLMs truly span the full lifecycle, design teams can expect AI support to shift from one-off idea generation to integrated toolchains, with prompt engineering becoming a core design skill.
  • The dominance of GPT-4 in the reviewed studies implies that conclusions about LLM-assisted design are largely conclusions about OpenAI's current models, so future reviews will need to track model shifts.
  • Best practices such as human-in-the-loop iteration, modular task decomposition, and multimodal grounding offer an actionable starting point for designing new tools even before formal standards exist.
  • The recurring challenges of hallucination, prompt instability, and explainability define a concrete research agenda centered on validation layers, structured prompting grammars, and evaluation benchmarks.
  • The fragmented tooling landscape suggests that early adoption is driven by individual plugins and bespoke systems, making shared evaluation standards a near-term necessity.

Reading between the lines

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

  • Because the relevance-based cutoff stops screening after a page shows fewer than five relevant titles out of ten, the corpus of 38 studies may undercount relevant work buried deeper in search rankings, so the GPT-4 dominance and the 2024 publication peak should be read as sample-dependent trends until a cutoff-free replication is done.
  • With 27 of 38 studies coming from ACM venues, the synthesis likely overrepresents HCI-conference perspectives; industry tooling inside commercial plugins or closed corporate systems is probably underreported.
  • A testable extension would be to run the same search on a newer 2025 to 2026 window and check whether Gemini, Claude, or open-weight models displace GPT-4 and whether the lifecycle mapping shifts toward more evaluation-stage use.
  • The review's best-practice list could be turned into a rubric for assessing new LLM design tools: check for prompt engineering support, human-in-the-loop editing, multimodal input handling, and explainability features.
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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

3 major / 6 minor

Summary. This paper reports a systematic literature review (SLR) of the role of large language models (LLMs) in UI/UX design. The authors define three research questions covering (RQ1) which LLMs are used and how they are integrated, (RQ2) emerging best practices, and (RQ3) limitations and risks. They searched ACM Digital Library, IEEE Xplore, and Scopus with an explicit Boolean query, applied a relevance-based cutoff during screening, and ultimately selected 38 peer-reviewed studies published between 2022 and 2025. The findings are organized around model frequencies (GPT-4 appearing most often, followed by GPT-3.5 and others), integration patterns (embedded tools, prompt-based interaction, lifecycle-wide use, multimodality, modular workflows, human-AI collaboration), best practices (prompt engineering, human-in-the-loop iteration, tool integration, modularity, multimodal grounding, trust and evaluation mechanisms), and challenges (hallucination, prompt instability, context loss, creativity constraints, trust issues, ethical/legal problems, tooling gaps). The paper concludes that LLMs are emerging collaborators across the full UI/UX design lifecycle and proposes directions for validation, prompt support, ethical safeguards, and evaluation standards.

Significance. If the review's sample is representative, the paper provides a useful and timely synthesis of a fast-moving area. Its strengths include a transparent protocol with clearly stated research questions, an explicit Boolean query, a PRISMA-style flow, a data-extraction framework, and a dedicated threats-to-validity section. The authors are also commendably candid about the limitations of their relevance-based cutoff and database choices. These features make the review reproducible in principle and give readers a clear basis for judging the evidence base. The central descriptive claims—that GPT-4 dominates current usage, that LLMs are used across ideation, prototyping, evaluation, and refinement, and that hallucinations and prompt instability are the main reported challenges—are plausible and well-aligned with the included studies. However, these claims rest on a sample that may be materially incomplete, and the paper does not quantify the potential impact of its selection shortcuts. The value of the review as a reference synthesis depends on the extent to which these concerns are resolved or explicitly bounded.

major comments (3)
  1. [Section 3.2.1; Tables 6 and 7] The relevance-based cutoff strategy assumes that search-result relevance decreases monotonically after the first few pages. For ACM, which returned 2,026 hits, only the first ~10 pages were title-screened; if relevant LLM-in-UI/UX papers appear beyond that horizon (e.g., because the engine ranks by date or by other criteria), then the model-frequency counts in Table 6 and the lifecycle-stage mapping in Table 7 are computed from a biased subset. The authors acknowledge this as a selection-bias threat in Section 3.5.1, but acknowledgment alone does not establish that the bias is negligible. I ask for a sensitivity analysis: for at least one database, screen a random sample of results beyond the cutoff (or compare against a query run without the cutoff on a smaller, targeted subset) and report whether the distribution of relevant studies and the resulting model/lifecycle patterns change materially. Without such a check, the central claim that LLMs—especially GPT-4—are used "throughout the design lifecycle" cannot be fully separated from the sampling rule.
  2. [Section 3.2.1 and Section 3.3.3] The treatment of Scopus is problematic in two respects. First, the protocol states that Scopus returned relevant results only in the first two pages, yet Figure 3 and the text indicate that no study from Scopus was ultimately included; this makes the Scopus filtering decision consequential. Because Scopus has broader coverage than ACM or IEEE in many areas, its complete exclusion could systematically omit relevant studies (e.g., journal articles from venues not indexed by ACM/IEEE). Second, the same subsection mentions that "Springer results were excluded entirely due to a lack of relevance" even though the search strategy lists only ACM, IEEE Xplore, and Scopus as the databases searched. This is an internal inconsistency: either Springer was searched and should be reported in the protocol and PRISMA flow, or it was not and the sentence should be removed. I request a clarification and, ideally, a documented justification or sensitivity check for the Scopus decision.
  3. [Section 3.3.1 and Section 3.3.2] The inclusion/exclusion process relies heavily on the authors' judgment of "scientific rigor," "relevance," and "credibility," but no formal quality-appraisal instrument or inter-rater reliability check is reported. The paper states that some studies were excluded for lacking methodological detail or for being domain-specific rather than generalizable, but it does not list the excluded studies or provide examples with enough specificity to allow a reader to verify the consistency of the judgments. For a systematic review, this is not necessarily fatal, but it does weaken the transparency of the selection stage. Please consider adding a short appendix or table that enumerates the full-text screened studies and the primary reason for exclusion, or at least provide a more detailed account of how the criteria were operationalized.
minor comments (6)
  1. [Section 3.3.1] There is an arithmetic inconsistency: starting from 338 papers, removing 7 duplicates and 1 non-English paper leaves 330, and removing 172 in the initial screening leaves 158, but the text says 159. Please correct this figure.
  2. [Section 3.2.1] The sentence "Scopus yielded relevant results primarily on the first two pages" is ambiguous because it is unclear whether those pages were fully included or whether the cutoff was applied at that point. Please specify the exact cutoff for each database, including page numbers and the number of titles reviewed.
  3. [Section 3.2.1] The mention of Springer is inconsistent with the list of searched databases; if Springer was indeed searched, its inclusion and exclusion should be reflected in the PRISMA flow and search strategy; otherwise, the sentence should be removed.
  4. [Section 4.1.3, Table 7] The lifecycle mapping would benefit from a clearer definition of the stages (e.g., whether "Research & Discovery" and "Ideation" are distinct or overlapping) and from explicit criteria for assigning a study to a stage. As written, the assignments appear reasonable but are not auditable.
  5. [Section 4.2.6] The text cites "[49, 39, 23]" for explainability and evaluation, but the reference numbering is occasionally used inconsistently (e.g., [23] is cited for both automatic feedback and heuristic evaluation in different places). A careful pass to ensure each citation supports the specific claim would improve accuracy.
  6. [Highlights and Abstract] The highlights state "GPT-4 and Gemini" while the abstract adds "PaLM"; please align the wording to avoid an apparent discrepancy.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the review synthesizes an external corpus and its only sampling heuristic is disclosed as a validity threat, not a derivation.

full rationale

This paper is a systematic literature review rather than a derivation: it extracts and synthesizes findings from 38 external peer-reviewed studies, and every reported result (LLM frequencies, lifecycle mapping, best practices, challenges) is a summary of what those included studies report, not a quantity computed from the review's own assumptions. The relevance-based cutoff in Section 3.2.1 is a screening heuristic, not a fitted parameter, and the authors explicitly disclose in Section 3.5.1 that it risks excluding relevant studies buried deeper in search rankings; that disclosure frames the cutoff as a threat to completeness and representativeness, not as a mechanism that defines the findings by construction. The 'first systematic literature review' claim is a novelty assertion contextualized against prior surveys, not an analytic premise, and the reference list contains no load-bearing self-citation by the present authors. No uniqueness theorem, ansatz, or normalization is imported from the authors' own prior work. The paper's substantive weakness is potential selection bias affecting external validity, which is a correctness concern rather than circularity. Because no step can be exhibited where a reported result reduces by definition or by self-citation to the review's own inputs, the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

This is a review paper, so the ledger lists methodological assumptions rather than fitted constants or postulated entities. The central synthesis rests on the completeness of the database search, the validity of the page-based cutoff heuristic, and the consistency of subjective screening judgments, none of which are independently verifiable from the paper itself.

assumptions (3)
  • domain assumption The three databases ACM Digital Library, IEEE Xplore, and Scopus provide sufficient coverage of relevant UI/UX and LLM research.
    Section 3.2 lists only these three databases; Springer is excluded entirely and Scopus is dropped after an initial relevance check, so the coverage assumption is load-bearing for the completeness of the synthesis.
  • ad hoc to paper The relevance-based cutoff strategy (stop after a page with fewer than 5 of 10 relevant titles) does not systematically exclude relevant studies.
    Section 3.2.1 introduces this non-standard heuristic as a pragmatic choice; the authors themselves list it as a selection-bias threat in Section 3.5.1, so the entire review depends on this fragile premise.
  • domain assumption Subjective judgments of 'scientific rigor, relevance, and credibility' during full-text screening are consistent and reproducible.
    Section 3.3.2 describes the screening criteria without an explicit rubric, inter-coder agreement, or a listing of excluded studies; the final 38-study set cannot be independently reconstructed from the information given.

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Cite this review

Pith. "Pith review of The role of large language models in UI/UX design: A systematic literature review." pith.science (2026). https://pith.science/paper/NFY3B6ON

@misc{pith2026250704469,
  author       = {Pith},
  title        = {Pith review of: The role of large language models in UI/UX design: A systematic literature review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NFY3B6ON}},
  note         = {Machine review of arXiv:2507.04469}
}
read the original abstract

This systematic literature review examines the role of large language models (LLMs) in UI/UX design, synthesizing findings from 38 peer-reviewed studies published between 2022 and 2025. We identify key LLMs in use, including GPT-4, Gemini, and PaLM, and map their integration across the design lifecycle, from ideation to evaluation. Common practices include prompt engineering, human-in-the-loop workflows, and multimodal input. While LLMs are reshaping design processes, challenges such as hallucination, prompt instability, and limited explainability persist. Our findings highlight LLMs as emerging collaborators in design, and we propose directions for the ethical, inclusive, and effective integration of these technologies.

Figures

Figures reproduced from arXiv: 2507.04469 by the authors.

Figure 1
Figure 1. PRISMA flow diagram illustrating the systematic selection process of studies on the use of large language models (LLMs) in UI/UX design. The diagram shows the initial search results across three academic databases (ACM Digital Library, IEEE Xplore, and Scopus), the application of relevance-based cutoff, duplicate removal, title and abstract screening based on defined exclusion criteria, and full-text screening using… view at source ↗
Figure 2
Figure 2. Distribution of fetched publications using our search query across ACM, Scopus, and IEEE Xplore databases. some studies described how LLMs were used to develop an educational app (which happens to contain UI/UX) but did not examine how or why these models improved the UI/UX design process, nor did they offer insights generalizable to other design projects. Hence, we did not feel that these studies yielded any lesson… view at source ↗
Figure 3
Figure 3. Number of studies retrieved and included from each digital library. The majority of included studies were sourced from ACM and IEEE Xplore, while no studies from Scopus met the exclusion and inclusion criteria. A, Ahmed. et al.: Preprint submitted to Elsevier Page 5 of 19 [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Distribution of journal vs. conference papers included for analysis [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Distribution of included studies across ACM and IEEE conferences. The bubble sizes represent the number of publications per venue [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Distribution of included studies by publication year. The number of relevant studies has grown rapidly since 2022, with a notable peak in 2024, indicating increasing academic attention to the role of LLMs in UI/UX design workflows. included a Citations field to note th…
Figure 7
Figure 7. Figure 7: Frequency of large language model (LLM) usage across reviewed UI/UX design studies. commonly used; however, they increasingly serve as base￾line or comparison models, reflecting the field’s evolution toward more capable systems. Google’s PaLM and Gemini families are al…
Figure 8
Figure 8. Figure 8: Distribution of LLM integration types across reviewed UI/UX studies (see Appendix 5). Each bar represents a study, with stacked segments indicating the presence of specific LLM integration types. creativity, and a clear evolution toward human-AI collabo￾ration and work…
Figure 9
Figure 9. Figure 9: Word cloud illustrating common best practices for integrating LLMs into UI/UX workflows from the reviewed studies [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]
Figure 10
Figure 10. Figure 10: Frequency of key challenges identified across studies on LLM integration in UI/UX workflows. Hallucinations, ethi￾cal concerns, and prompt-related issues emerged as the most commonly reported limitations. standardized usability metrics (e.g., SUM, USE) to validate out…

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.