The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index; none of the 45 sources its claims cite is checked for support yet.
Writing with AI Lowers Psychological Ownership, but Longer Prompts Can Help
Open accessAbstract
The feeling of something belonging to someone is called "psychological ownership." A common assumption is that writing with generative AI lowers psychological ownership, but the extent to which this occurs and the role of prompt length are unclear. We report on two experiments to examine the relationship between psychological ownership and prompt length. Participants wrote short stories either completely by themselves or wrote prompts of varying lengths. Results show that when participants wrote longer prompts, they had higher levels of psychological ownership. Their comments suggest they thought more about their prompts, often adding more details about the plot. However, benefits plateaued when prompt length was 75-100% of the target story length. To encourage users to write longer prompts, we propose augmenting the prompt submission button so it must be held down a long time if the prompt is short. Results show that this technique is effective at increasing prompt length.
Overview
CoChat Summary
TL;DR
Writing with a generative AI assistant lowers psychological ownership compared with writing independently, but requiring or encouraging longer prompts can raise it. Across three experiments, psychological ownership increased with prompt length up to about a 150-word minimum, then plateaued, and a press-and-hold submission delay nudged users to write longer prompts.
Key findings
- In Experiment 1, psychological ownership was lowest for the 3-word AI prompt condition (m = 1.80, sd = 1.13), then rose for 50–100 words (m = 3.90, sd = 1.61), 150–200 words (m = 4.57, sd = 1.70), and was highest for no-AI solo writing (m = 6.29, sd = 0.80).
- Longer prompts were associated with higher psychological ownership: Word Count correlated with Psychological Ownership at r_s = .72, p < .001; Text Similarity at r_s = .75, p < .001; Mental Demand at r_s = .65, p < .001; and Effort at r_s = .66, p < .001.
- Experiment 2 found a plateau beyond a 150-word minimum: ai-150 (m = 4.69, sd = 1.64), ai-175 (m = 4.70, sd = 1.73), and ai-200 (m = 4.54, sd = 1.81) did not differ significantly, and all AI conditions remained lower than no-AI (m = 6.19, sd = 0.99).
- Experiment 3 showed that time delays increased prompt length: 20-sec (m = 76.91, sd = 68.13) and 60-sec (m = 74.57, sd = 63.80) produced significantly higher Final Word Count than none (m = 34.66, sd = 46.56) and 0-sec (m = 28.09, sd = 32.69), while the strict 150-word minimum condition had the highest Final Word Count (m = 164.65, sd = 19.54).
Methods
- Experiment 1 used a within-subjects design with 31 valid Prolific participants after excluding 10 (24%). Participants wrote 150–200-word short stories either alone or with GPT-3.5 Turbo using prompts of 3 words, 50–100 words, or 150–200 words. Measures included a four-item Psychological Ownership composite (Cronbach’s α = 0.96), NASA-TLX items, logged Duration, Word Count, and Text Similarity via Google’s Universal Sentence Encoder; analysis used Friedman and Wilcoxon signed-rank tests with Holm’s corrections and Spearman correlations.
- Experiment 2 used 34 valid participants after excluding 5 (13%), with the same protocol and measures (α = 0.92) but prompt conditions of 150–200, 175–200, and 200–250 words.
- Experiment 3 used 156 valid participants after excluding 42 (21%) in a mixed design: condition was between-subjects (none n = 32, 0-sec n = 32, 20-sec n = 31, 60-sec n = 32, 150-word minimum n = 29) and trial was within-subjects (1–4). Participants wrote prompts to generate a 150–200-word story; delayed submission required holding the button, with delay proportional to prompt length. Logged measures included Final Word Count, Submission Attempts, Attempted Word Count, and Attempted Duration; analysis used Aligned Rank Transform, Kruskal-Wallis, Mann-Whitney U, Holm’s corrections, and Spearman correlations.
Limitations
- The paper states that it explored prompt length within a single prompt, whereas real users likely refine generated text over multiple prompts; it calls for exploring word count across multiple messages in a conversation.
- The paper states that the Experiment 2 plateau may be due to plateauing semantic text similarity and may disappear as models improve; the authors tested GPT-4 Turbo and 4-o with the same prompts and observed no differences in Text Similarity.
- The paper states that Experiment 3 may lack ecological validity: no additional rewards were given for longer prompts, Prolific crowdworkers may be more conscientious and willing to write longer prompts or less likely to abandon the system, and 20- and 60-second delays may not transfer to real-world systems; shorter delays, feedback, and explanations may also affect willingness to wait.
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Claims the paper makes
0 Valid · 3 Not valid · 26 Not checked · 101 Own results and interpretations
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The second experiment used even higher word minimums, yet psychological ownership plateaued.”
- Also stated in: § 5.2.1 Psychological Ownership. · hedged
“Overall, psychological ownership when writing with AI seems to plateau beyond a 150 word minimum (Figure 8).”
§ Writing with AI Lowers Psychological Ownership, but Longer Prompts Can Help > 1 INTRODUCTION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § 5.2.1 Psychological Ownership. · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using a similar creative writing task, we conducted an experiment with 20-and 60-second delays, and found prompt length increased compared to having no delays.”
- Also stated in: § 6 ENCOURAGING LONGER PROMPTS IN A REAL INTERFACE · hedged
“We ran a pilot experiment (n=90, 11-18 per condition) to better understand which modifications were the most promising for increasing prompt length, and our preliminary results 4 suggested that integrating a time delay into the prompt submission button could be effective (Figure C.2).”
§ Writing with AI Lowers Psychological Ownership, but Longer Prompts Can Help > 1 INTRODUCTION
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § 6 ENCOURAGING LONGER PROMPTS IN A REAL INTERFACE · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Results showed that as participants wrote more to meet higher word minimums, they had higher levels of psychological ownership.”
- Also stated in: § 4.1 Psychological Ownership · plain
“Overall, writing more words improved feelings of psychological ownership (Figure 3).”
§ Writing with AI Lowers Psychological Ownership, but Longer Prompts Can Help > 1 INTRODUCTION
plainstates an association- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § 4.1 Psychological Ownership · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Despite having the same word limits, we also observed a significant difference between ai-150 and no-ai.”
§ Writing with AI Lowers Psychological Ownership, but Longer Prompts Can Help > 4.2.2 Word Count.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our piloted interaction techniques all attempted to nudge users towards a known word minimum of 150 words as our first two experiments suggested that psychological ownership plateaued at 150 words for the given task.”
§ Writing with AI Lowers Psychological Ownership, but Longer Prompts Can Help > 9.2 Implementation Approaches and Challenges
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“• 200-250 words with GPT, representing 100-150% of the maximum final story word count.”
§ Writing with AI Lowers Psychological Ownership, but Longer Prompts Can Help > 5.1 Choice of Prompt Lengths
Reported 150 · recomputed 125
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“𝜒 2 3,𝑁 =31 = 99.00,𝑝 < .001,𝑊 = 1.00”
§ Writing with AI Lowers Psychological Ownership, but Longer Prompts Can Help > A EXPERIMENT 1: ANALYSIS DETAILS
Reported 99 · recomputed 93
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Writing with AI Lowers Psychological Ownership, but Longer Prompts Can Help > 8.2 Learning Effect
“Specifically, significant main effects of trial on Submission Attempts and Attempted Word Count and post hoc tests only revealed significant differences between trial 1 and all other trials.”
§ Writing with AI Lowers Psychological Ownership, but Longer Prompts Can Help > C PRELIMINARY INTERFACE MODIFICATIONS AND INTERACTION TECHNIQUES
“Table D.2: Omnibus and post hoc statistical tests for Experiment 3: (a) Final Word Count, (b) Submission Attempts, (c) Attempted Word Count for trial = 1.”
Citations
Cites
ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 1×
“We believe these results are promising and can positively impact human-AI collaborations.”
The aligned rank transform for nonparametric factorial analyses using only anova procedures
Wobbrock, J. O., Findlater, L., Gergle, D., & Higgins, J. J. (2011) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1978963 · Cited by 2,854
Toward a Theory of Psychological Ownership in Organizations
Pierce, J. L., Kostova, T., & Dirks, K. T. (2001) · The Academy of Management Review · doi:10.5465/amr.2001.4378028 · Cited by 2,546
The State of Psychological Ownership: Integrating and Extending a Century of Research
Pierce, J. L., Kostova, T., & Dirks, K. T. (2003) · Review of General Psychology · doi:10.1037/1089-2680.7.1.84 · Cited by 2,038 · cited 6×
“Psychological ownership may explain an illusory superiority cognitive bias ; specifically, Blau and Caspi [5, 6] showed that writers believe that edits made by a collaborator decreases quality of text, but paradoxically, the writer believes their own edits improve their collaborator’s text.”
InfluentialProceedings for the Annual Meeting of the Cognitive Science Society
Dziemianko, M., Clarke, A. D. F., & Keller, F. (2013) · Cited by 1,726
Douglas, B. D., Ewell, P. J., & Brauer, M. (2023) · PLOS ONE · doi:10.1371/journal.pone.0279720 · Cited by 1,532
Universal Sentence Encoder for English
Cer, D., Yang, Y., Kong, S.-Y., Hua, N., Limtiaco, N., St. John, R., Constant, N., Guajardo-Cespedes, M., Yuan, S., Tar, C., et al. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/d18-2029 · Cited by 1,277
Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021) · arXiv · doi:10.48550/arxiv.2102.09692 · Cited by 1,124
An Aligned Rank Transform Procedure for Multifactor Contrast Tests
Elkin, L. A., Kay, M., Higgins, J. J., & Wobbrock, J. O. (2021) · The 34th Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3472749.3474784 · Cited by 664
A study on tolerable waiting time: how long are Web users willing to wait?
Nah, F. F.-H. (2004) · Behaviour & Information Technology · doi:10.1080/01449290410001669914 · Cited by 593
Wason, P. C., & Evans, J. S. B. T. (1974) · Cognition · doi:10.1016/0010-0277(74)90017-1 · Cited by 575
Wordcraft: Story Writing With Large Language Models
Yuan, A., Coenen, A., Reif, E., & Ippolito, D. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511105 · Cited by 374
Response time and display rate in human performance with computers
Shneiderman, B. (1984) · ACM Computing Surveys · doi:10.1145/2514.2517 · Cited by 347
Lee, M., Liang, P., & Yang, Q. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502030 · Cited by 345
User learning and performance with marking menus
Kurtenbach, G., & Buxton, W. (1994) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/191666.191759 · Cited by 340
Oh, C., Song, J., Choi, J., Kim, S., Lee, S., & Suh, B. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174223 · Cited by 301
Mirowski, P., Mathewson, K. W., Pittman, J., & Evans, R. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581225 · Cited by 240 · cited 1×
“Metapho-ria [15] suggests metaphors while writing poems, but writers felt less psychological ownership when using it, especially when “the suggestions were particularly good.””
A field trial of privacy nudges for facebook
Wang, Y., Leon, P. G., Acquisti, A., Cranor, L. F., Forget, A., & Sadeh, N. (2014) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557413 · Cited by 237
DirectGPT: A Direct Manipulation Interface to Interact with Large Language Models
Masson, D., Malacria, S., Casiez, G., & Vogel, D. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642462 · Cited by 187
Self-citationDraxler, F., Werner, A., Lehmann, F., Hoppe, M., Schmidt, A., Buschek, D., & Welsch, R. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3637875 · Cited by 186 · cited 1×
“Draxler et al. [12] asked participants to write postcards entirely by themselves or to edit or choose postcards pre-written by AI before “signing” the postcard.”
Structuring, Aggregating, and Evaluating Crowdsourced Design Critique
Luther, K., Tolentino, J.-L., Wu, W., Pavel, A., Bailey, B. P., Agrawala, M., Hartmann, B., & Dow, S. P. (2015) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2675133.2675283 · Cited by 176
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Self-citationMetaphoria: An Algorithmic Companion for Metaphor Creation
Gero, K. I., & Chilton, L. B. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300526 · Cited by 146
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Lee, M., Gero, K. I., Chung, J. J. Y., Shum, S. B., Raheja, V., Shen, H., Venugopalan, S., Wambsganss, T., Zhou, D., Alghamdi, E. A., et al. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642697 · Cited by 136 · cited 1×
“Psychological ownership was an important factor for these writers, and some designs were poorly received because they “overstep the boundaries” of the human writer.”
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Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models
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Caspi, A., & Blau, I. (2010) · Social Psychology of Education · doi:10.1007/s11218-010-9141-z · Cited by 102
Olson, J. S., Wang, D., Olson, G. M., & Zhang, J. (2017) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3038919 · Cited by 101
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How people write together (groupware)
Posner, I. R., & Baecker, R. (1992) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.1992.183420 · Cited by 97
Halfaker, A., Kittur, A., Kraut, R. E., & Riedl, J. (2009) · International Symposium on Wikis and Open Collaboration (WikiSym) · doi:10.1145/1641309.1641332 · Cited by 95
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Yim, S., Wang, D., Olson, J., Vu, V., & Warschauer, M. (2017) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2998181.2998356 · Cited by 65
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Rezwana, J., & Maher, M. L. (2023) · Creativity and Cognition · doi:10.1145/3591196.3593364 · Cited by 60
Yeh, C., Ramos, G., Ng, R., Huntington, A., & Banks, R. (2024) · arXiv.org · doi:10.48550/arxiv.2402.08855 · Cited by 51
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Birnholtz, J. P., Steinhardt, S. B., & Pavese, A. (2013) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2470654.2466123 · Cited by 47
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Larsen-Ledet, I., & Korsgaard, H. (2019) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-019-09359-8 · Cited by 43
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Wang, Q., Battocchi, A., Graziola, I., Pianesi, F., Tomasini, D., Zancanaro, M., & Nass, C. (2006) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1180995.1181041 · Cited by 34
Measuring ownership of creative versus academic writing: Implications for interdisciplinary praxis
Nicholes, J. (2017) · Writing in Practice · doi:10.62959/wip-03-2017-08 · Cited by 31
Low Latency Anonymous Communication – How Long Are Users Willing to Wait?
Köpsell, S. (2006) · Lecture notes in computer science · doi:10.1007/11766155_16 · Cited by 30
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Yen, R., Zhao, J., & Vogel, D. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713822 · Cited by 29
Self-citationLehmann, F., Markert, N., Dang, H., & Buschek, D. (2022) · Mensch und Computer 2022 · doi:10.1145/3543758.3543947 · Cited by 25
Parachute: Evaluating Interactive Human-LM Co-writing SystemsPreprint
Shen, H., & Wu, T. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.06333 · Cited by 20
Comparing Smartphone Speech Recognition and Touchscreen Typing for Composition and Transcription
Foley, M., Casiez, G., & Vogel, D. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376861 · Cited by 17
Self-citationKumaran, S. R. K., Yin, Y., & Bailey, B. P. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449098 · Cited by 15
Longer Delays in Rehearsal-based Interfaces Increase Expert Use
Lewis, B., & Vogel, D. (2020) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3418196 · Cited by 11
Self-citationInspo: Writing with Crowds Alongside AI
Huang, C.-Y., Gautam, S., Brooks, S. M., Lin, Y.-F., Knearem, T., & Huang, T.-H. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720193 · Cited by 10
Research works printed but not linked: 4
- Ina Blau and Avner Caspi. 2009. Sharing and Collaborating with Google Docs: The Influence of Psychological Ownership, Responsibility, and Student’s Attitudes on Outcome Quality. In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education. Association for the Advancement of Computing in Education (AACE), 3329–3335.
- Ina Blau and Avner Caspi. 2009. What Type of Collaboration Helps? Psychological Ownership, Perceived Learning and Outcome Quality of Collaboration using Google Docs. In Proceedings of the Chais Conference on Instructional Technologies Research, Vol. 12. 48–55.
- Mark D Dunlop, Emma Nicol, Andreas Komninos, Prima Dona, and Naveen Durga. 2016. Measuring Inviscid Text Entry using Image Description Tasks. In Proceedings of the 2016 CHI Workshop on Inviscid Text Entry and Beyond.
- Serge Egelman, David Molnar, Nicolas Christin, Alessandro Acquisti, Cormac Herley, and Shriram Krishnamurthi. 2010. Please Continue to Hold. In Ninth Workshop on the Economics of Information Security.
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Lee, H. M., Yadav, D., Lee, S., Govindarazan, K., Chen, C., & Sundar, S. S. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719725 · Cited by 13
Kim, N., & Ji, Y. G. (2026) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2026.103771 · Cited by 12
Metadata
- Authors
- Joshi, N., & Vogel, D.
- Affiliations
- University of Waterloo (CA)
- Venue
- Proceedings of the 7th ACM Conference on Conversational User Interfaces
- Publisher
- ACM
- Published
- 2025-07-07
- Pages
- 1-17
- OpenAlex
- W4412055679
- Cited by
- 50
- References
- 49
- Citations by year
- 2026: 41 · 2025: 9
- FWCI
- 30.93
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Conference on Conversational User Interfaces (CUI) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Natural Sciences and Engineering Research Council of Canada (RGPIN-2024-03827)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Digital Media and Visual Art
- Keywords
- psychological ownership, prompt length, short story writing, user interface design, human-AI collaboration
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