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REVIEW 3 major objections 6 minor 61 references

Identifying Semantic Similarity for UX Items from Established Questionnaires Using ChatGPT-4

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper claims that ChatGPT-4 can reconstruct UX factors, filter questionnaire items for predefined UX concepts, and uncover semantic overlaps between UX concepts, and that all three research questions are confirmed.

desk verdict A useful extension of the authors' prior ChatGPT-4 UX work, but the load-bearing claim that the classifications are 'useful' rests on qualitative inspection and needs a human-rater baseline to be a real result. read the letter →

arxiv 2411.13616 v1 pith:MZJCXKTG submitted 2024-11-20 cs.HC

Stefan Graser , Martin Schrepp , Stephan Böhm This is my paper · ORCID
classification cs.HC
keywords userexperienceUXquestionnairessemanticsimilarityChatGPT-4largelanguagemodelstextualfactorsgenerativeAI
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 asks whether ChatGPT-4 can take over the labor-intensive job of understanding the semantic structure of UX questionnaires. On a pool of 408 items from 19 established questionnaires, it runs six prompts to reconstruct UX factors, a generic prompt adapted per concept to filter items for Learnability, Efficiency, Usefulness, Dependability, and Stimulation, and a second pool of 135 standardized "I perceive the product as ..." items to map overlaps between eleven common UX concepts. The authors report that all three investigations produced plausible, useful results and confirm the three research questions. If the claim holds, researchers could explore semantic dependencies across questionnaires quickly and cheaply, without manually comparing hundreds of item formulations.

What carries the argument

The machinery is a four-step procedure in which ChatGPT-4 supplies every semantic judgment. Step one collects 408 items from 19 of 40 established UX questionnaires, excluding semantic differentials and items tied to a specific interface or product type. Step two applies six successive prompts to the first item pool, moving from a broad classification to detailed subtopics, an improved categorization, a comparison with the 16 consolidated UX quality aspects, and finally a generalized holistic topic structure. Step three uses a generic prompt with a replaceable concept definition to filter the best-matching items from the same pool. Step four builds a second pool of 135 artificial items of the form "I perceive the product as <adjective>", asks ChatGPT-4 in three separate runs which adjectives belong to each of eleven UX concepts, and keeps only adjectives assigned consistently across all runs; the resulting concept–adjective links form the semantic similarity network. The named outputs are the classification hierarchies, the filtered item lists, and the overlap network.

What would settle it

Give the same 408-item and 135-adjective pools to several UX experts, have them independently produce the classifications, item lists, and concept assignments, and compute agreement with ChatGPT-4's outputs; low agreement would show the model's judgments do not track expert semantics. A simpler stability check would be to rerun the prompts with reworded formulations and see whether the concept-overlap network, including the Aesthetics–Value overlap and the Clarity-mediated link to usability, survives.

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Extended reading notes

Core claim

The central claim is that a large language model, ChatGPT-4, can perform meaningful semantic analysis of UX questionnaire items, and that this is useful for UX research. In the first investigation, iterative prompting turned 408 items into a hierarchy of topics and subtopics that the authors judge largely coherent with a published consolidation of 16 UX quality aspects, although some hedonic aspects such as Novelty and Identity are not well captured. In the second, prompt-based filtering retrieved top-10 items for predefined concepts, with strong alignment for classical usability concepts and weaker alignment for Stimulation, which the authors attribute to the usability-oriented item pool. In the third, adjectives standardized into "I perceive the product as X" statements produced a concept-connection network that reproduces known empirical findings, including the strong Aesthetics–Value overlap and an indirect link from Aesthetics to usability through Clarity. The paper concludes that applying ChatGPT-4 was useful for all three tasks and that the three research questions are confirmed.

Load-bearing premise

The paper assumes that ChatGPT-4's semantic judgments are a valid stand-in for how UX experts understand the meaning of questionnaire items, without measuring agreement against expert human ratings.

Editorial extensions

If this is right

  • UX researchers could use ChatGPT-4 as a quick first pass to map the semantic landscape of a large item pool before selecting or constructing a questionnaire.
  • Ad-hoc surveys could be assembled by retrieving existing, validated items from a pool instead of writing new ones, with human review reserved for the few misfits the model produces.
  • The semantic concept network provides a bridge between semantic and empirical views of UX, since known empirical correlations such as the aesthetics–usability dependency reappear in the purely semantic map.
  • The near match between semantic assignments and the empirically constructed UEQ/UEQ+ scales suggests that a questionnaire built on semantic similarity alone is a realistic future artifact.
  • Because the method is cheap and quick, classifications can be run repeatedly and treated as explorative hypotheses that guide, rather than replace, expert judgment.

Reading between the lines

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

  • The authors do not measure run-to-run reliability; a direct extension is to run each prompt several times and report agreement across runs as a stability score.
  • The absence of a human-rater baseline means the paper's positive conclusion rests on the model's agreement with expert semantics; a testable extension is to compare ChatGPT-4's assignments with expert sortings of the same items using an agreement index such as Cohen's kappa.
  • The standardized adjective format could be exported to other construct domains, for example trust in automation, to map concept overlaps there; that extrapolation goes beyond what the paper claims.
  • The Clarity-mediated link between Aesthetics and usability, visible in the concept network, yields a concrete hypothesis for user studies: systematically varying layout clarity should shift perceived usability more than perceived beauty.
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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. The paper explores whether ChatGPT-4 can be used to analyze semantic similarities among UX questionnaire items. In the first investigation, ChatGPT-4 is asked to (re)construct UX factors by classifying 408 items from 19 established questionnaires into topics and subtopics through six successively refined prompts. In the second, a generic prompt is adapted to filter items representing predefined UX concepts (Learnability, Efficiency, Usefulness, Dependability, Stimulation) from the same item pool. In the third, 135 artificial standardized items of the form "I perceive the product as X" are used to map semantic connections among 11 common UX concepts, with each prompt run three times and only items consistently assigned in all three runs retained. The authors report that the generated classifications and item assignments are plausible and align well with existing UX knowledge, concluding in Section VIII-A that "applying ChatGPT was useful for conducting all three tasks" and that the three research questions can be confirmed.

Significance. If the central claim were quantitatively supported, the paper would provide a low-cost, scalable method for exploring the semantic structure of UX questionnaires and for supporting questionnaire selection and ad-hoc item generation. The third investigation is the most methodologically promising part: the artificial standardization of items, the three-run consistency filter, and the explicit comparison with empirically constructed UEQ+ scales are sensible design choices. The authors also clearly articulate the distinction between semantic and empirical similarity in Section II-B, which is an important conceptual contribution. However, the paper currently offers no quantitative validation of the core assertion that ChatGPT-4's outputs are meaningful, and it therefore reads more as a demonstration than as a validated research result.

major comments (3)
  1. [Section VIII-A, with Sections V and VI] The central conclusion that "applying ChatGPT was useful for conducting all three tasks" and that RQ1 and RQ2 are "confirmed" is not supported by the evidence presented. In Sections V and VI, each prompt was run only once, and the evaluations (e.g., "plausible" in V-B1, "fit well" in VI-B, and the discussion of misclassifications) are the authors' qualitative judgments. No inter-rater reliability, no comparison against expert human raters, and no quantitative metric such as precision, recall, or agreement are reported. Since the authors themselves note in Section VIII-A that ChatGPT-4 is non-deterministic, a single run cannot establish that the observed outputs reflect stable semantic competence rather than one arbitrary draw.
  2. [Section VI-B] The item-filtering results are vulnerable to lexical cueing. The prompt for Efficiency explicitly contains the word "efficiently," and the concept descriptions for the other dimensions contain the target words (e.g., "control," "secure," "stimulating"). The reported top-10 lists include items that contain these exact words or near-synonyms; for example, item 7 under Efficiency, "The processing times of the software are easy for me to estimate," contains "processing times" and is acknowledged by the authors to be a Dependability item. Without a control condition (e.g., prompts that avoid using the target words) or a human baseline, the results do not allow the reader to distinguish semantic understanding from keyword matching, which is a load-bearing issue for RQ2.
  3. [Section VII-B] The external validation against empirically constructed UEQ+ scales is qualitative only. The statement that the correspondence is "remarkably close" is not accompanied by any counts, confusion matrix, or statistical measure. The three-run consistency filter in Section VII-A is a useful reliability step, but it documents within-model consistency under a fixed prompting condition; it does not measure agreement with expert judgment or with empirically derived scale assignments, so it cannot by itself establish the validity of the semantic assignments.
minor comments (6)
  1. [Section V-B2] The phrase "it is more precious" should presumably be "it is more precise."
  2. [Appendix A3] There is a typo in the Stimulation topic: "I continued to use thr application out of curiosity" should read "the application."
  3. [Section IV] The list of the 19 included questionnaires is not provided, so the reader cannot verify the exclusion criteria or reproduce the 408-item pool; adding an appendix with the questionnaire names and item list would improve reproducibility.
  4. [Figure 3] Figure 3 is difficult to read because of the small font size and dense connection lines; a supplementary table listing the shared items for each pair of UX concepts would improve clarity.
  5. [Section VII] The transformation of statement items into adjectives ("we removed all other parts of the item and kept only the positive adjective") is described informally; a more explicit rule for handling negations and multi-word phrases would reduce ambiguity.
  6. [Section II-B] The claim that LLMs "use word embeddings ... to calculate semantic similarity" is stated as a fact about ChatGPT-4's internal mechanism; since that mechanism is not directly observable, the wording should be more cautious.

Circularity Check

1 steps flagged · score 2.0 of 10

No central circularity; one minor self-referential validation occurs when the authors' own consolidated UX factor list is fed into prompt5 and then used as the benchmark to declare the AI-generated topics logical.

  1. self citation load bearing [Section V-B-5 ('Prompt5: Comparison Towards Existing Consolidation'), Table II and its concluding paragraph]
    "In particular, we inserted the existing UX quality aspects and formulated the prompt as follows: 'In literature, I can find such a list with 16 UX factors. —inserted the defined quality aspects (see Table I) [13] —. Can you compare this list with your categorization and contrast these lists?' ... Nevertheless, there are many similarities between the two consolidations, and thus, the AI-generated topics by ChatGPT can be considered logical."

    The reference standard used to declare the AI-generated topics 'logical' is the consolidated UX factor list from [13], a prior work sharing an author with the present paper, and this same list is inserted into the ChatGPT prompt immediately before the comparison. The model is asked to relate its earlier categorization to a list supplied by the experimenters, so the observed alignment is partly a consequence of the prompt content rather than an independent rediscovery of the factors. This makes the prompt5-based validation self-referential. The circularity is limited because prompts1-4 and prompt6 generate categories without receiving the factor list, and RQ2 and RQ3 rely on other checks, so the central derivation is not forced by this step.

full rationale

The paper's main derivation chain is not circular. For RQ1, prompts1-4 and prompt6 ask ChatGPT to classify 408 item texts into semantic topics without providing the target factor names; the resulting topics are therefore not defined by the inputs. For RQ2, the generic prompt describes a UX concept and asks the model to select fitting items from the same raw item pool, and the paper assesses the selections against expert expectations—this is an empirical use-case, not a fitted parameter renamed as a prediction. For RQ3, the artificial items are constructed from adjectives in existing questionnaires and the model is prompted with concept descriptions; the final check against empirically built UEQ+ scales is an external benchmark, even though the item pool is derived from related questionnaires. No equation-level reduction, fitted input, or uniqueness import is present. The one notable self-referential element is the prompt5 comparison: the authors' own consolidated factor list from [13] is fed into the prompt and then used as the reference for saying the AI-generated topics are 'logical.' This is a minor self-citation that partially contaminates one validation comparison, but it is not load-bearing for the overall conclusion that GenAI is useful for the three tasks, which also rests on qualitative inspection and the external UEQ+ comparison. Concerns about non-determinism and the lack of a human-rater baseline are substantive validity issues, but they are not circularity.

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

The paper contributes a qualitative demonstration rather than a derivation; no fitted parameters or new theoretical entities are introduced. The load-bearing assumptions are domain assumptions about the validity of LLM judgments, the representativeness of the item pools, and the completeness of the chosen concept list.

assumptions (4)
  • domain assumption LLM semantic judgments are a valid proxy for human semantic similarity of UX items.
    The whole method assumes ChatGPT-4's clustering and filtering reflect meaningful semantic relations; the paper only checks plausibility by inspection, not against human raters.
  • domain assumption The selected item pool (408 items from 19 questionnaires) and the 135 standardized adjectives are representative of the UX questionnaire space.
    Section IV; exclusions of semantic differentials and product-specific items shape results, and the 135 items are artificially constructed from positive adjectives only.
  • domain assumption The 11 UX concepts used as a reference frame (Learnability, Efficiency, etc.) are the relevant dimensions for UX.
    These concepts come from the authors' own prior work [13] and UEQ+ [44]; the semantic overlap analysis cannot detect relations outside this frame.
  • domain assumption Considering an adjective related to a concept only if it was assigned in all three ChatGPT runs is a sufficient reliability rule.
    Section VII-A; this threshold reduces noise but is a heuristic with no justification.

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Cite this review

Pith. "Pith review of Identifying Semantic Similarity for UX Items from Established Questionnaires Using ChatGPT-4." pith.science (2026). https://pith.science/paper/MZJCXKTG

@misc{pith2026241113616,
  author       = {Pith},
  title        = {Pith review of: Identifying Semantic Similarity for UX Items from Established Questionnaires Using ChatGPT-4},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MZJCXKTG}},
  note         = {Machine review of arXiv:2411.13616}
}
read the original abstract

Questionnaires are a widely used tool for measuring the user experience (UX) of products. There exists a huge number of such questionnaires that contain different items (questions) and scales representing distinct aspects of UX, such as efficiency, learnability, fun of use, or aesthetics. These items and scales are not independent; they often have semantic overlap. However, due to the large number of available items and scales in the UX f ield, analyzing and understanding these semantic dependencies can be challenging. Large language models (LLM) are powerful tools to categorize texts, including UX items. We explore how ChatGPT-4 can be utilized to analyze the semantic structure of sets of UX items. This paper investigates three different use cases. In the first investigation, ChatGPT-4 is used to generate a semantic classification of UX items extracted from 40 UX questionnaires. The results demonstrate that ChatGPT-4 can effectively classify items into meaningful topics. The second investigation demonstrates ChatGPT-4's ability to filter items related to a predefined UX concept from a pool of UX items. In the third investigation, a second set of more abstract items is used to describe another classification task. The outcome of this investigation helps to determine semantic similarities between common UX concepts and enhances our understanding of the concept of UX. Overall, it is considered useful to apply GenAI in UX research

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.