Pith. sign in

REVIEW 3 major objections 4 minor 86 references

Timing Matters: How Using LLMs at Different Timings Influences Writers' Perceptions and Ideation Outcomes in AI-Assisted Ideation

T0 review · 3 major / 4 minor · reviewed 2026-08-08 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Using an LLM from the start of an ideation task makes writers produce LLM-shaped ideas and feel less ownership and self-credit, while delaying LLM help preserves both.

desk verdict The perception findings are solid, but the abstract's central quantity claim is undermined by a protocol asymmetry and should not be taken at face value. read the letter →

arxiv 2502.06197 v1 pith:KTULYJ7S submitted 2025-02-10 cs.HC

Peinuan Qin , Chi-Lan Yang , Jingshu Li , Jing Wen , Yi-Chieh Lee This is my paper · ORCID
classification cs.HC
keywords AI-assistedideationLLMtimingideafixationautonomyownershipcreativeself-efficacycreditattributionhuman-AIcollaboration
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 that the timing of LLM assistance in an ideation task changes both what people produce and how they feel about it. In an experiment with 60 writers who generated supporting ideas for health claims, using the LLM from the very start made participants' final ideas overlap more with the LLM's suggestions and reduced their sense of autonomy, ownership, creative self-efficacy, and self-credit. Writing first and bringing in the LLM afterward raised those perceptions and produced more ideas. The authors locate the mechanism in a chain: delayed LLM use increases perceived autonomy, autonomy increases ownership, and ownership drives creative self-efficacy, self-credit, and idea quantity. They recommend an incubation stage in AI-assisted ideation tools, with LLM output kept out of view until users have formed initial ideas.

What carries the argument

The load-bearing mechanism is a serial mediation chain estimated with structural equation modeling: LLM usage timing to perceived autonomy to sense of ownership to creative self-efficacy to quantity of ideas, with ownership also predicting self-credit and both autonomy and ownership predicting credit assigned to the LLM. Autonomy fully mediated the effect on ownership, and ownership fully mediated the effect on creative self-efficacy, so the model's core claim is that timing works through perceived control rather than directly through content. The study's similarity measure is TF-IDF cosine similarity between each participant's ideas and the LLM's suggestions, and idea diversity uses the complement of mean pairwise embedding similarity; both are computed on the final written ideas. The experimental apparatus also carries part of the mechanism: in the delayed condition the AI panel was locked until a participant had saved at least three independently generated ideas, while the beginning condition had no such gate.

What would settle it

Run the same ideation task with identical pre-LLM output requirements in both arms, for instance requiring at least three saved ideas before the AI panel unlocks in both conditions or in neither, and compare idea counts, similarity, autonomy, and ownership. The timing claim would be falsified if the differences in quantity and perceptions vanish under matched requirements; it would be strengthened if the perceptual and similarity gaps persist.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that LLM assistance is not neutral in timing: introducing the model after a period of independent ideation, rather than at the beginning, yielded significantly higher perceived autonomy (p=0.007) and ownership (p=0.001), preserved creative self-efficacy between pre- and post-task measures, increased self-credit and reduced credit attributed to the LLM, and reduced the semantic overlap between users' ideas and LLM-generated ideas (means 0.648 vs 0.899, p=0.006). Idea quantity was higher in the delayed condition but only marginally so (p=0.063). A structural equation model with serial mediators showed that timing raised autonomy, autonomy fully mediated ownership, ownership fully mediated creative self-efficacy and self-credit, and creative self-efficacy predicted idea quantity; similarity with LLM ideas was directly affected by timing, not by these mediators. The interpretation the authors draw is that early LLM exposure induces idea fixation and over-reliance, while delayed exposure lets users keep their own conceptual space and treats the LLM as an elaborator.

Load-bearing premise

The central timing comparison assumes that the two conditions differ only in when the LLM appeared, but the delayed condition also required participants to write at least three ideas before the AI unlocked while the beginning condition imposed no such minimum; if that requirement is what produced the extra ideas, the quantity advantage is an artifact.

Editorial extensions

If this is right

  • Tool designers should consider hiding or delaying AI-generated suggestions until a user has produced initial ideas, to protect autonomy and ownership.
  • If the mediation chain holds, interventions that raise perceived autonomy, not just those that delay AI, should cascade to ownership, creative self-efficacy, self-credit, and idea quantity.
  • Early LLM use produces ideas that are more similar to the LLM's own output, so studies of AI-assisted ideation should report overlap with the model, not only idea counts or diversity.
  • The nonsignificant diversity result suggests the fixation effect appears as convergence toward the LLM's ideas rather than a measurable narrowing of the user's own idea space.
  • Delayed use did not lower idea quantity; it marginally increased it, so the common assumption that AI access always adds volume may depend on when it is offered.

Reading between the lines

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

  • The marginal quantity finding is confounded by the protocol: the delayed condition forced at least three pre-LLM ideas while the beginning condition did not, so a replication with matched minimums is needed to separate timing effects from output requirements.
  • High similarity in the beginning condition may be partly mechanical: with LLM examples visible from the start, users are likely to select and lightly rephrase suggestions, so overlap reflects selection, not only cognitive fixation.
  • The mediation model suggests a testable design loop in which an ideation tool dynamically gauges perceived autonomy and withholds AI output when autonomy is low, reproducing the delayed-condition benefits without a fixed waiting period.
  • The same timing logic may not transfer to constrained creative tasks such as design or image generation, where divergent thinking has different bottlenecks; the paper's own limitations acknowledge task-dependence.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper reports a between-subjects online experiment (N=60, recruited via Prolific) comparing two timings of LLM use in a health-claim ideation task: C_before, where the LLM was available from the start, and C_after, where the LLM was enabled only after participants generated at least three ideas independently. The authors measure idea quantity, TF-IDF/cosine similarity between participant and LLM ideas, idea diversity, creative self-efficacy, autonomy, ownership, and credit attribution, and they fit a structural equation model with autonomy, ownership, and creative self-efficacy as serial mediators. Open-ended responses are analyzed thematically to triangulate the quantitative results. The central claims are that using LLMs at the beginning reduces the number of original ideas and lowers creative self-efficacy and self-credit, mediated by changes in autonomy and ownership, and that delaying LLM use is therefore preferable.

Significance. The perception findings are the strongest part of the paper: C_after participants reported significantly higher autonomy (p=0.007), ownership (p=0.001), creative self-efficacy (p=0.018), and self-credit (p=0.029), and lower credit to the LLM (p=0.007). The similarity result (C_before M=0.899 vs C_after M=0.648, p=0.006) is a direct behavioral indicator that early LLM exposure increases lexical overlap with LLM output. The study is a controlled experiment with counterbalanced topics, a practice phase to equate prompting familiarity, and mixed-method triangulation. However, the headline quantity claim is not secured: the between-condition difference in idea count is only marginal (p=0.063) and is confounded by the asymmetric minimum-idea requirement in the C_after protocol. With revisions that reframe the contribution around the similarity and perception findings and properly qualify the mediation analysis, this could be a useful empirical contribution to AI-assisted ideation.

major comments (3)
  1. [Section 3.3, Section 3.5.2, Section 4.1] The quantity-of-ideas finding is not identifiable as a timing effect because of an asymmetric protocol requirement. In C_after, participants had to generate at least three distinct and original supporting ideas before the AI panel unlocked, whereas in C_before there was no such minimum independent-idea requirement. Section 3.5.2 then counts every distinct submitted idea 'regardless of whether it was generated independently or with the assistance of the LLM.' Consequently, C_after's mean of 5.567 necessarily includes the three pre-AI ideas, while C_before's mean of 3.867 is not protected by any floor. The between-group test is marginal (t(58) = -1.897, p = 0.063, d = -0.490), so the observed difference could be entirely produced by the instruction to generate three ideas before LLM access. The abstract's wording 'reduced the number of original ideas' is additionally not supported by this outcome: the count is of all distinct ideas, not of independently original ones, and originality is operationalized only as inverse similarity to LLM output. This issue is load-bearing because the 'delay LLM usage' design implication in Section 5.1 draws on the quantity result. Please reanalyze the data (for example, comparing only ideas generated after AI access, or adjusting for the floor) or reframe the claims to rest on the similarity and perception findings.
  2. [Section 3.5.3, Section 4.1] The similarity score is treated as a direct measure of originality, but it actually measures lexical overlap with LLM output. A participant can independently produce an idea that overlaps with the LLM because both rely on shared background knowledge, and low TF-IDF similarity can occur for off-topic or incoherent ideas as well as for original ones. The sentence in Section 4.1 that using the LLM from the start 'made participants have more overlapped ideas with LLM' is supported; the subsequent inference that the C_after condition 'fostered original contributions' is not. The paper should either validate the similarity measure against human originality ratings or soften the originality language throughout the abstract, introduction, and discussion.
  3. [Section 4.3, Table 2] There is an internal inconsistency in the reported effect of LLM Usage Timing on Quantity of Ideas: Estimate 1.563, SE 0.905, z = 1.727, p = 0.084, but 95% CI [0.120, 3.210]. A 95% confidence interval that excludes zero is not compatible with a two-tailed p of 0.084 under standard Wald-type intervals; please clarify the estimation method (for example, percentile bootstrap) and report the exact p-value and CI consistently. More generally, the model with 60 participants, three serial mediators, and multiple distal outcomes is near the limits of reliable SEM estimation; statements such as 'fully mediated' and 'completely derived from' should be replaced with more cautious language, particularly where the direct effect is nonsignificant (for example, timing to ownership, p = 0.074) but the sample is small.
minor comments (4)
  1. [Section 4.1] The reported Welch t-test for similarity, t(48.36) = 4.778, p = 0.006, is internally inconsistent: for these degrees of freedom, a t-value of 4.78 corresponds to a much smaller two-sided p (about 1e-5). Please verify and report the correct value, even though the conclusion of significance would not change.
  2. [Section 3.5.3, Table 1] Step 6.1.1 of the pseudocode says 'Compute the magnitude of the TF-IDF vector |v_j| as the similarity score', but a vector magnitude is not a cosine-similarity score. Please clarify how the column-vector magnitudes are computed and averaged, and explain why the reported scores (e.g., 0.899 and 0.648) lie in the range expected for cosine similarities.
  3. [Section 3.5.7] The autonomy measure uses only two items, one of which is reverse-worded ('I felt like AI was writing the text and I was assisting'). Please state whether the reverse item was recoded before averaging, and consider reporting the inter-item correlation as a supplement to Cronbach's alpha for a two-item scale.
  4. [Section 6] The limitations section does not mention the asymmetric minimum-idea requirement or the operationalization of originality as similarity to LLM output. Given that both issues affect the central claims, they should be acknowledged explicitly in the limitations.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: this is an empirical between-subjects experiment, not a derivation whose output is equivalent to its input; the quantity-measurement confound is a validity concern, not circular reasoning.

full rationale

This paper makes no first-principles derivation claim. Its central results are observed between-condition differences in measured outcomes: idea count (Section 3.5.2), TF-IDF cosine similarity with LLM ideas (Section 3.5.3), idea diversity (Section 3.5.4), and Likert-scale perceptions (Sections 3.5.5-3.5.8). None of these measures is defined in terms of the independent variable (timing of LLM access), nor is any outcome fitted from the data and then relabeled as a prediction. The SEM in Section 4.3 is fitted to the same experimental data and is presented as a mediation model, not as an independent prediction; its paths are justified by external literature, and the model does not by construction force the reported effects. The similarity metric is an operationalization of overlap with LLM output; the claim that higher similarity indicates lower originality is an interpretive labeling of the measure, but the between-condition difference is an empirical finding, not an algebraic consequence of the metric. No load-bearing self-citation chain appears: background citations such as Kohn and Smith and McGuire et al. motivate the study but do not supply the present result, and no uniqueness theorem or prior-author claim is invoked to forbid alternative interpretations. The abstract's phrase 'reduced the number of original ideas' overstates the quantity result, and the Section 3.3 asymmetry—C_after participants were required to generate at least three independent ideas before the AI panel unlocked while C_before participants had no such floor—is a plausible confound for the marginal quantity difference (p = 0.063). However, a confound is an internal-validity threat, not circularity: the idea count is not defined in terms of the condition, and the observed difference was not produced by construction. The paper's own limitations section acknowledges task simplicity and uncontrolled individual differences, which further supports treating these as ordinary empirical limitations rather than circular reasoning.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

This is an empirical HCI study, not a derivation. The ledger captures measurement assumptions and model specification choices: self-report scales, the similarity proxy, and the assumed causal structure of the mediation model. No new entities are postulated.

free parameters (2)
  • SEM path coefficients = autonomy->ownership 0.632, ownership->self-efficacy 0.366, self-efficacy->quantity 1.603
    Estimated from 60-participant sample; not cross-validated.
  • TF-IDF similarity aggregation method = magnitude of column vectors
    Design choice by authors; no external benchmark validates this as a measure of fixation.
assumptions (3)
  • domain assumption Self-report scales validly measure autonomy, ownership, and creative self-efficacy
    Scales adapted from prior literature (for example Tierney and Farmer 2002, Draxler et al. 2023); no behavioral validation in this study.
  • domain assumption TF-IDF cosine similarity is an acceptable proxy for originality and fixation
    Used because it captures term overlap; authors checked with transformer embeddings and found similar trends, but both are indirect proxies.
  • domain assumption The SEM causal ordering is correct
    Mediators were not experimentally manipulated; alternative causal orders (for example self-efficacy influencing autonomy) are plausible.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Timing Matters: How Using LLMs at Different Timings Influences Writers' Perceptions and Ideation Outcomes in AI-Assisted Ideation." pith.science (2026). https://pith.science/paper/KTULYJ7S

@misc{pith2026250206197,
  author       = {Pith},
  title        = {Pith review of: Timing Matters: How Using LLMs at Different Timings Influences Writers' Perceptions and Ideation Outcomes in AI-Assisted Ideation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KTULYJ7S}},
  note         = {Machine review of arXiv:2502.06197}
}
read the original abstract

Large Language Models (LLMs) have been widely used to support ideation in the writing process. However, whether generating ideas with the help of LLMs leads to idea fixation or idea expansion is unclear. This study examines how different timings of LLM usage - either at the beginning or after independent ideation - affect people's perceptions and ideation outcomes in a writing task. In a controlled experiment with 60 participants, we found that using LLMs from the beginning reduced the number of original ideas and lowered creative self-efficacy and self-credit, mediated by changes in autonomy and ownership. We discuss the challenges and opportunities associated with using LLMs to assist in idea generation. We propose delaying the use of LLMs to support ideation while considering users' self-efficacy, autonomy, and ownership of the ideation outcomes.

Figures

Figures reproduced from arXiv: 2502.06197 by the authors.

Figure 1
Figure 1. Experiment workflow of our study. Participants experienced a pre-survey, a practice task, an ideation task, and a post [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Overview of the task interface presented to participants during the ideation task. The interface consists of three main [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Comparison between 𝐶𝑏𝑒 𝑓 𝑜𝑟𝑒 and 𝐶𝑎 𝑓 𝑡𝑒𝑟 in (a) idea quantity, (b) idea similarity with LLM, and (c) participant’s idea diversity. The violin plots represent the distribution, while the overlaid boxplots show the median and interquartile ranges. Significant differences between the conditions are indicated by p-value annotations, where ns >= 0.05, * < 0.05, ** < 0.01, *** < 0.001 [PITH_FULL_IMAGE:figures/full_fig_p… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Comparison between pre- and post creative self [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Comparison between 𝐶𝑏𝑒 𝑓 𝑜𝑟𝑒 and 𝐶𝑎 𝑓 𝑡𝑒𝑟 in perceptions of (a) creative self-efficacy, (b) autonomy, (c) ownership, (d) self credit, and (e) credit to LLM. The violin plots represent the distribution, while the overlaid boxplots show the median and interquartile range…
Figure 6
Figure 6. Figure 6: This figure illustrates the structural equation model (SEM) used to understand how LLM usage timing affects (in gray) [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Comparison between 𝐶𝑏𝑒 𝑓 𝑜𝑟𝑒 and 𝐶𝑎 𝑓 𝑡𝑒𝑟 in idea sim￾ilarity with LLM. (a) represents the detailed similarity distri￾bution, where the distribution of ideas generated by partici￾pants in 𝐶𝑎 𝑓 𝑡𝑒𝑟 are concentrated in areas with lower scores while that in 𝐶𝑏𝑒 𝑓 𝑜𝑟𝑒 are …

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

86 extracted references · 67 canonical work pages

  1. [1]

    Barrett R Anderson, Jash Hemant Shah, and Max Kreminski. 2024. Homogeniza- tion effects of large language models on human creative ideation. In Proceedings of the 16th conference on creativity & cognition . 413–425

    2024

  2. [2]

    Quentin André, Ziv Carmon, Klaus Wertenbroch, Alia Crum, Douglas Frank, William Goldstein, Joel Huber, Leaf Van Boven, Bernd Weber, and Haiyang Yang

  3. [3]

    A Bandura. 1997. Self-efficacy: The Exercise of Control. New York: W. H Freeman. Self-efficacy: the exercise of control. New York: WH Freeman (1997)

    1997

  4. [4]

    Albert Bandura et al. 1986. Social foundations of thought and action. Englewood Cliffs, NJ 1986, 23-28 (1986), 2

    1986

  5. [5]

    Albert Bandura and Nancy E Adams. 1977. Analysis of self-efficacy theory of behavioral change. Cognitive therapy and research 1, 4 (1977), 287–310

    1977

  6. [6]

    Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology 3, 2 (2006), 77–101

    2006

  7. [7]

    Lijia Chen, Pingping Chen, and Zhijian Lin. 2020. Artificial intelligence in education: A review. Ieee Access 8 (2020), 75264–75278

    work page 2020

  8. [8]

    Li-Yuan Chiou, Peng-Kai Hung, Rung-Huei Liang, and Chun-Teng Wang. 2023. Designing with AI: an exploration of co-ideation with image generators. In Proceedings of the 2023 ACM designing interactive systems conference . 1941–1954

    work page 2023

Show all 86 references
  1. [9]

    Elizabeth Clark, Anne Spencer Ross, Chenhao Tan, Yangfeng Ji, and Noah A Smith. 2018. Creative writing with a machine in the loop: Case studies on slogans and stories. In23rd International Conference on Intelligent User Interfaces. 329–340

    2018

  2. [10]

    Mark Coeckelbergh. 2020. Artificial intelligence, responsibility attribution, and a relational justification of explainability. Science and engineering ethics 26, 4 (2020), 2051–2068

    2020

  3. [11]

    Samuel Rhys Cox, Yunlong Wang, Ashraf Abdul, Christian Von Der Weth, and Brian Y. Lim. 2021. Directed diversity: Leveraging language embedding dis- tances for collective creativity in crowd ideation. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Syste...

    2021

  4. [12]

    Edward L Deci and Richard M Ryan. 2012. Self-determination theory. Handbook of theories of social psychology 1, 20 (2012), 416–436

    2012

  5. [13]

    Ismail Dergaa, Helmi Ben Saad, Jordan M Glenn, Badii Amamou, Mohamed Ben Aissa, Noomen Guelmami, Feten Fekih-Romdhane, and Karim Chamari

  6. [14]

    Zijian Ding, Arvind Srinivasan, Stephen MacNeil, and Joel Chan. 2023. Fluid transformers and creative analogies: Exploring large language models’ capacity for augmenting cross-domain analogical creativity. In Proceedings of the 15th Conference on Creativity and Cognition . 489–505

    2023

  7. [15]

    Anil R Doshi and Oliver Hauser. 2023. Generative artificial intelligence enhances creativity. A vailable at SSRN (2023)

    2023

  8. [16]

    Fiona Draxler, Anna Werner, Florian Lehmann, Matthias Hoppe, Albrecht Schmidt, Daniel Buschek, and Robin Welsch. 2023. The AI Ghostwriter Ef- fect: Users Do Not Perceive Ownership of AI-Generated Text But Self-Declare as Authors. arXiv preprint arXiv:2303.03283 (2023)

    2023 arXiv

  9. [17]

    Andrew R du Rocher. 2020. Active learning strategies and academic self-efficacy relate to both attentional control and attitudes towards plagiarism. Active Learn- ing in Higher Education 21, 3 (2020), 203–216

    2020

  10. [18]

    Marylène Gagné and Edward L Deci. 2005. Self-determination theory and work motivation. Journal of Organizational behavior 26, 4 (2005), 331–362

    2005

  11. [19]

    A Shaji George, T Baskar, and P Balaji Srikaanth. 2024. The Erosion of Cognitive Skills in the Technological Age: How Reliance on Technology Impacts Critical Thinking, Problem-Solving, and Creativity. (2024)

    2024

  12. [20]

    Katy Ilonka Gero and Lydia B Chilton. 2019. Metaphoria: An algorithmic com- panion for metaphor creation. In Proceedings of the 2019 CHI conference on human factors in computing systems . 1–12

    2019

  13. [21]

    J Guilford. 1950. Creativity. American Psychology. 5 (9), 444–454

    1950

  14. [22]

    Joy Paul Guilford. 1967. The nature of human intelligence. New York: Macgraw Hill (1967)

    1967

  15. [23]

    Shirley Hayati, Minhwa Lee, Dheeraj Rajagopal, and Dongyeop Kang. 2024. How Far Can We Extract Diverse Perspectives from Large Language Models?. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 5336–5366

    2024

  16. [24]

    Cheng-Zhi Anna Huang, Hendrik Vincent Koops, Ed Newton-Rex, Monica Din- culescu, and Carrie J Cai. 2020. AI song contest: Human-AI co-creation in songwriting. arXiv preprint arXiv:2010.05388 (2020)

    2020 arXiv

  17. [25]

    Angel Hsing-Chi Hwang. 2022. Too late to be creative? AI-empowered tools in creative processes. In CHI Conference on Human Factors in Computing Systems Extended Abstracts. 1–9

    2022

  18. [26]

    Maurice Jakesch, Advait Bhat, Daniel Buschek, Lior Zalmanson, and Mor Naaman

  19. [27]

    David G Jansson and Steven M Smith. 1991. Design fixation. Design studies 12, 1 (1991), 3–11

    1991

  20. [28]

    Kimberly S Jaussi, Amy E Randel, and Shelley D Dionne. 2007. I am, I think I can, and I do: The role of personal identity, self-efficacy, and cross-application of experiences in creativity at work. Creativity Research Journal 19, 2-3 (2007), 247–258

    2007

  21. [29]

    Kenneth R Koedinger, John R Anderson, William H Hadley, and Mary A Mark

  22. [30]

    Nicholas W Kohn and Steven M Smith. 2011. Collaborative fixation: Effects of others’ ideas on brainstorming. Applied Cognitive Psychology 25, 3 (2011), 359–371

    2011

  23. [31]

    Chinmay Kulkarni, Steven P Dow, and Scott R Klemmer. 2013. Early and repeated exposure to examples improves creative work. In Design thinking research: Building innovation eco-systems. Springer, 49–62

    2013

  24. [32]

    Arto Laitinen and Otto Sahlgren. 2021. AI systems and respect for human autonomy. Frontiers in artificial intelligence 4 (2021), 705164

    2021

  25. [33]

    Bart Lamiroy and Emmanuelle Potier. 2022. Lamuse: Leveraging Artificial Intel- ligence for Sparking Inspiration. In International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar) . Springer, 148–161

    2022

  26. [34]

    Mina Lee, Percy Liang, and Qian Yang. 2022. Coauthor: Designing a human- ai collaborative writing dataset for exploring language model capabilities. In Proceedings of the 2022 CHI conference on human factors in computing systems . 1–19

    2022

  27. [35]

    Julie S Linsey, Ian Tseng, Katherine Fu, Jonathan Cagan, Kristin L Wood, and Christian Schunn. 2010. A study of design fixation, its mitigation and perception in engineering design faculty. (2010)

    2010

  28. [36]

    Gro Ellen Mathisen. 2011. Organizational antecedents of creative self-efficacy. Creativity and Innovation Management 20, 3 (2011), 185–195

    2011

  29. [37]

    Marian Mazzone and Ahmed Elgammal. 2019. Art, creativity, and the potential of artificial intelligence. In Arts, Vol. 8. MDPI, 26

    2019

  30. [38]

    Jack McGuire, David De Cremer, and Tim Van de Cruys. 2024. Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence. Scientific Reports 14, 1 (2024), 18525

    2024

  31. [39]

    Piotr Mirowski, Kory W Mathewson, Jaylen Pittman, and Richard Evans. 2023. Co-Writing Screenplays and Theatre Scripts with Language Models: Evaluation by Industry Professionals. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems . 1–34

    2023

  32. [40]

    Antonija Mitrovic, Brent Martin, and Michael Mayo. 2002. Using evaluation to shape ITS design: Results and experiences with SQL-Tutor. User Modeling and User-Adapted Interaction 12 (2002), 243–279

    2002

  33. [41]

    Bernard A Nijstad and Wolfgang Stroebe. 2006. How the group affects the mind: A cognitive model of idea generation in groups. Personality and social psychology review 10, 3 (2006), 186–213

    2006

  34. [42]

    Chiyem Lucky Nwanzu and Sunday Samson Babalola. 2022. The influence of psychological ownership and creative self-efficacy on employee creative perfor- mance. Pakistan Journal of Commerce and Social Sciences (PJCSS) 16, 1 (2022), 34–49

    2022

  35. [43]

    Jon L Pierce, Tatiana Kostova, and Kurt T Dirks. 2001. Toward a theory of psychological ownership in organizations. Academy of management review 26, 2 CHI ’25, April 26-May 1, 2025, Yokohama, Japan Peinuan Qin, Chi-Lan Yang, Jingshu Li, Jing Wen, and Yi-Chieh Lee (2001), 298–310

    2001

  36. [44]

    Reza Pourreza, Apratim Bhattacharyya, Sunny Panchal, Mingu Lee, Pulkit Madan, and Roland Memisevic. 2023. Painter: Teaching Auto-regressive Language Models to Draw Sketches. arXiv preprint arXiv:2308.08520 (2023)

    2023 arXiv

  37. [45]

    Carina Prunkl. 2024. Human Autonomy at Risk? An Analysis of the Challenges from AI. Minds and Machines 34, 3 (2024), 26

    2024

  38. [46]

    A Terry Purcell and John S Gero. 1996. Design and other types of fixation.Design studies 17, 4 (1996), 363–383

    1996

  39. [47]

    Othman Sbai, Mohamed Elhoseiny, Antoine Bordes, Yann LeCun, and Camille Couprie. 2018. Design: Design inspiration from generative networks. In Proceed- ings of the European Conference on Computer Vision (ECCV) Workshops . 0–0

    2018

  40. [48]

    Daniel L Schwartz, Catherine C Chase, Marily A Oppezzo, and Doris B Chin

  41. [49]

    Daniel L Schwartz and Taylor Martin. 2004. Inventing to prepare for future learning: The hidden efficiency of encouraging original student production in statistics instruction. Cognition and instruction 22, 2 (2004), 129–184

    2004

  42. [50]

    Mathura Shanmugasundaram and Arunkumar Tamilarasu. 2023. The impact of digital technology, social media, and artificial intelligence on cognitive functions: a review. Frontiers in Cognition 2 (2023), 1203077

    2023

  43. [51]

    Pao Siangliulue, Joel Chan, Krzysztof Z Gajos, and Steven P Dow. 2015. Pro- viding timely examples improves the quantity and quality of generated ideas. In Proceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition . 83–92

    2015

  44. [52]

    Monika Simmler. 2024. Responsibility gap or responsibility shift? The attri- bution of criminal responsibility in human–machine interaction. Information, Communication & Society 27, 6 (2024), 1142–1162

    2024

  45. [53]

    Dilpreet Singh, Nina Rajcic, Simon Colton, and Jon McCormack. 2019. Camera obscurer: generative art for design inspiration. In Computational Intelligence in Music, Sound, Art and Design: 8th International Conference, EvoMUSART 2019, Held as Part of EvoStar 2019, Leipzig, Germa...

    2019

  46. [54]

    Nikhil Singh, Guillermo Bernal, Daria Savchenko, and Elena L Glassman. 2022. Where to hide a stolen elephant: Leaps in creative writing with multimodal machine intelligence. ACM Transactions on Computer-Human Interaction (2022)

    2022

  47. [55]

    Christina Starmans. 2023. Autonomy, the moral circle, and the limits of ownership. Behavioral & Brain Sciences 46 (2023)

    2023

  48. [56]

    Jonathan Stolk and Janie Harari. 2014. Student motivations as predictors of high-level cognitions in project-based classrooms. Active Learning in Higher Education 15, 3 (2014), 231–247

    2014

  49. [57]

    Timo Strohmann, Dominik Siemon, and Susanne Robra-Bissantz. 2017. brAIn- storm: Intelligent assistance in group idea generation. In Designing the Digital Transformation: 12th International Conference, DESRIST 2017, Karlsruhe, Germany, May 30–June 1, 2017, Proceedings 12 . Spri...

    2017

  50. [58]

    Min Tang, Weiping Hu, and Huan Zhang. 2017. Creative self-efficacy from the Chinese perspective: Review of studies in mainland China, Hong Kong, Taiwan, and Singapore. The creative self (2017), 237–257

    2017

  51. [59]

    Pamela Tierney and Steven M Farmer. 2002. Creative self-efficacy: Its potential antecedents and relationship to creative performance. Academy of Management journal 45, 6 (2002), 1137–1148

    2002

  52. [60]

    Luis A Vasconcelos and Nathan Crilly. 2016. Inspiration and fixation: Questions, methods, findings, and challenges. Design Studies 42 (2016), 1–32

    2016

  53. [61]

    Luis A Vasconcelos, Maria A Neroni, Carlos Cardoso, and Nathan Crilly. 2018. Idea representation and elaboration in design inspiration and fixation experiments. International Journal of Design Creativity and Innovation 6, 1-2 (2018), 93–113

    2018

  54. [62]

    Samangi Wadinambiarachchi, Ryan M Kelly, Saumya Pareek, Qiushi Zhou, and Eduardo Velloso. 2024. The Effects of Generative AI on Design Fixation and Divergent Thinking. arXiv preprint arXiv:2403.11164 (2024)

    2024 arXiv

  55. [63]

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems 35 (2022), 24824–24837

    2022

  56. [64]

    Roosa Wingström, Johanna Hautala, and Riina Lundman. 2023. Redefining creativity in the era of AI? Perspectives of computer scientists and new media artists. Creativity Research Journal (2023), 1–17

    2023

  57. [65]

    CHEN YANG. 2024. Teacher Autonomy, Creative Self-efficacy, and Innovative Behavior: Perspectives from Chinese University EFL Teachers.Arab World English Journal 15, 2 (2024)

    2024

  58. [66]

    Nicolas Yax, Hernan Anlló, and Stefano Palminteri. 2024. Studying and improving reasoning in humans and machines. Communications Psychology 2, 1 (2024), 51

    2024

  59. [67]

    Robert J Youmans and Thomaz Arciszewski. 2014. Design fixation: Classifications and modern methods of prevention. AI EDAM 28, 2 (2014), 129–137

    2014

  60. [68]

    Ann Yuan, Andy Coenen, Emily Reif, and Daphne Ippolito. 2022. Wordcraft: story writing with large language models. In 27th International Conference on Intelligent User Interfaces. 841–852

    2022

  61. [69]

    Fan Zhuo. 2021. Human-machine co-creation on artistic paintings. In 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI) . IEEE, 316–319. Timing Matters CHI ’25, April 26-May 1, 2025, Yokohama, Japan A Supplementary Materials Figure 7: Compa...

    2021

  62. [75]

    Applying your urine can worsen the pain for the jellyfish, and it might spread the venom to other areas of the jellyfish

  63. [76]

    Applying urine to a jellyfish sting can disrupt the delicate balance of the venom’s pH and potentially trigger the release of more toxins

    Urine has a slightly acidic to neutral pH, while jellyfish venom often contains proteins and toxins that are sensitive to pH changes. Applying urine to a jellyfish sting can disrupt the delicate balance of the venom’s pH and potentially trigger the release of more toxins. This...

  64. [77]

    However, it doesn’t contain any specialized compounds or properties that can effectively counteract the venom’s effects

    Urine may temporarily provide a cooling sensation due to its moisture, creating a placebo effect that the pain is reducing. However, it doesn’t contain any specialized compounds or properties that can effectively counteract the venom’s effects. Proper first aid for jellyfish s...

  65. [78]

    When a person applies urine to a jellyfish sting, it may spread the venom to unaffected areas, leading to a larger portion of the skin becoming affected and more painful

  66. [79]

    The pH level of urine varies, making it unpredictable and unreliable for neutralizing jellyfish venom, whereas vinegar has a consistent acidity that works well

  67. [80]

    Urine may also introduce bacteria to the wound, increasing the risk of infection, especially if the person has urinary tract infections or other medical conditions

  68. [81]

    Urine can contain substances that further irritate the jellyfish venom and cause additional discomfort

    Despite the popular belief that urine can relieve the pain of a jellyfish sting, research suggests that it may actually worsen the situation. Urine can contain substances that further irritate the jellyfish venom and cause additional discomfort

  69. [82]

    The high salt content in urine can potentially increase the pain by dehydrating the affected area and intensifying the sting sensation

    Urinating on a jellyfish sting may not provide any significant relief. The high salt content in urine can potentially increase the pain by dehydrating the affected area and intensifying the sting sensation

  70. [83]

    This can result in further complications and prolong the healing process

    Urine may introduce bacteria or other harmful substances to the jellyfish sting, leading to an increased risk of infection. This can result in further complications and prolong the healing process

  71. [84]

    Relying on urinating as a solution for jellyfish stings may delay proper treatment. Instead of seeking medical attention or using proven remedies like vinegar or seawater to neutralize the venom, wasting time on urine application can prevent timely and effective intervention

  72. [85]

    Jellyfish stings are caused by specialized cells on their tentacles called nematocysts, which inject venom into the skin upon contact

    Applying urine to a jellyfish sting can actually worsen the pain and potentially spread the venom to other areas of the jellyfish. Jellyfish stings are caused by specialized cells on their tentacles called nematocysts, which inject venom into the skin upon contact

  73. [86]

    Vinegar is effective in neutralizing toxins, while urine lacks such properties

    Proper first aid for jellyfish stings involves rinsing the affected area with seawater, carefully removing any tentacles using tweezers or a gloved hand, and soaking the area in hot water for pain relief. Vinegar is effective in neutralizing toxins, while urine lacks such properties

  74. [1997]

    International Journal of Artificial Intelligence in Education 8 (1997), 30–43

    Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education 8 (1997), 30–43

    1997

  75. [2011]

    Journal of educational psychology 103, 4 (2011), 759

    Practicing versus inventing with contrasting cases: The effects of telling first on learning and transfer. Journal of educational psychology 103, 4 (2011), 759

    2011

  76. [2018]

    Customer needs and solutions 5 (2018), 28–37

    Consumer choice and autonomy in the age of artificial intelligence and big data. Customer needs and solutions 5 (2018), 28–37

    2018

  77. [2023]

    In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

    Co-writing with opinionated language models affects users’ views. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems . 1–15

    2023

  78. [2024]

    Frontiers in Psychology 15 (2024), 1259845

    From tools to threats: a reflection on the impact of artificial-intelligence chatbots on cognitive health. Frontiers in Psychology 15 (2024), 1259845

    2024

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.