CoChat
Sign Up
Cited by 233Creativity in Education and NeuroscienceArtificial Intelligence ApplicationsArtificial Intelligence in Games

Artificial muses: Generative artificial intelligence chatbots have risen to human-level creativity

Open access
Haase, Jennifer, Hanel, Paul H.P.·2023·Journal of Creativity
DOI 10.1016/j.yjoc.2023.100066
Open this paper in CoChat — free account

Abstract

A widespread view is that Artificial Intelligence cannot be creative. We tested this assumption by comparing human-generated ideas with those generated by six Generative Artificial Intelligence (GAI) chatbots: alpa.ai, Copy.ai, ChatGPT (versions 3 and 4), Studio.ai, and YouChat. Humans and a specifically trained AI independently assessed the quality and quantity of ideas. We found no qualitative difference between AI and human-generated creativity, although there are differences in how ideas are generated. Interestingly, 9.4% of humans were more creative than the most creative GAI, GPT-4. Our findings suggest that GAIs are valuable assistants in the creative process. Continued research and development of GAI in creative tasks is crucial to fully understand this technology's potential benefits and drawbacks in shaping the future of creativity. Finally, we discuss the question of whether GAIs are capable of being “truly” creative.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.yjoc.2023.100066

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

No citation lists for this paper

No citation lists for this paper yet — checked again daily.

References 50 · Cited by 233

Research works printed but not linked: 49

  1. Amabile, T. M. (1982). Social psychology of creativity: A consensual assessment technique. Journal of Personality and Social Psychology, 43 (5), 997. http://refhub.elsevier.com/S2713-3745(2300025-0/sbref0001)
  2. Amabile, T. M. (2017). In pursuit of everyday creativity. The Journal of Creative Behavior, 51 (4), 335 -337. https://doi.org/10.1002/jocb.200 · DOI · Paper page
  3. Amabile, T. M., & Pratt, M. G. (2016). The dynamic componential model of creativity and innovation in organizations: Making progress, making meaning. Research in Organizational Behavior, 36 , 157 -183. https://doi.org/10.1016/j.riob.2016.10.001 · DOI · Paper page
  4. Baer, J. (2015). The importance of domain-specific expertise in creativity. Roeper Review, 37 (3), 165 -178. https://doi.org/10.1080/02783193.2015.1047480 · DOI · Paper page
  5. Bellaiche L., Shahi R., Turpin M., Ragnhildstveit A., Sprockett S., Barr N., Christensen A., & Seli, P. (2023). Humans vs. AI: Whether and why we prefer human-created compared to AI-created artwork. 10.31234/osf.io/f9upm. · DOI
  6. Benedek, M. (2018). The Neuroscience of Creative Idea Generation. In Z. Kapoula, E. Volle, J. Renoult, & M. Andreatta (Eds.), Exploring Transdisciplinarity in Art and
  7. Benedek, M., Bruckdorfer, R., & Jauk, E. (2020). Motives for creativity: Exploring the what and why of everyday creativity. The Journal of Creative Behavior, 54 (3), 610 -625. https://doi.org/10.1002/jocb.396 · DOI
  8. Botella, M., Nelson, J., & Zenasni, F. (2019). It is time to observe the creative process: how to use a creative process report diary (CRD). The Journal of Creative Behavior, 53 (2), 211 -221. https://doi.org/10.1002/jocb.172 · DOI · Paper page
  9. Bruno, C., & Bruno, C. (2022). Digital creativity dimension: A new domain for creativity. Creativity in the design process: exploring the influences of the digital evolution (pp. 29 -42). Springer International Publishing. http://refhub.elsevier.com/S2713-3745(2300025-0/sbref0010)
  10. Civit, M., Civit-Masot, J., Cuadrado, F., & Escalona, M. J. (2022). A systematic review of artificial intelligence-based music generation: Scope, applications, and future trends. Expert Systems with Applications, 209 , Article 118190. https://doi.org/10.1016/j. eswa.2022.118190 · DOI
  11. Cope, D. (2005). Computer models of musical creativity . Mit Press Cambridge. http://refhub.elsevier.com/S2713-3745(2300025-0/sbref0013)
  12. Corazza, G. E. (2016). Potential originality and effectiveness: The dynamic definition of creativity. Creativity Research Journal, 28 (3), 258 -267. https://doi.org/10.1080/ 10400419.2016.1195627
  13. Cropley, D. H., Medeiros, K. E., Damadzic, A., Henriksen, D., & Mishra, P. (2023). The intersection of human and artificial creativity. Creative provocations: speculations on the future of creativity, technology & learning (pp. 19 -34). Springer International Publishing. 10.1007/978-3-031-14549-0\_2. · DOI
  14. Dale, R. (2021). GPT-3: What ' s it good for? Natural Language Engineering, 27 (1), 113 -118. https://doi.org/10.1017/S1351324920000601 · DOI
  15. Dell ' Aversana P. (2023). GPT-3: A new cooperation scenario between humans and machines. Benefits and limitations of GPT-3 as a coding virtual assistant. ResearchGate. 10.13140/RG.2.2.32450.04800. · DOI
  16. DiPaola, S., & McCaig, G. (2016). Using artificial intelligence techniques to emulate the creativity of a portrait painter . Electronic Visualisation and the Arts. https://doi.org/ 10.14236/ewic/EVA2016.32 · DOI
  17. Else, H. (2023). Abstracts written by ChatGPT fool scientists. Nature . https://doi.org/ 10.1038/d41586-023-00056-7 · DOI · Paper page
  18. Frosio, G. (2023). The Artificial Creatives: The Rise of Combinatorial Creativity from Dall-E to GPT-3. Martha Garcia-Murillo, Ian MacInnes, and Andrea Renda. In Handbook of Artificial Intelligence at Work: Interconnections and Policy Implications . Edward Elgar, Forthcoming), Queen ' s University Belfast Law Research Paper. htt ps://papers.ssrn.com/abstract = 4350802.
  19. Gruner, D. T., & Csikszentmihalyi, M. (2019). Engineering creativity in an age of artificial intelligence. The Palgrave Handbook of Social Creativity Research , 447 -462. http://refhub.elsevier.com/S2713-3745(2300025-0/sbref0024)
  20. Guzik, E. E., Byrge, C., & Gilde, C. (2023). The originality of machines: AI takes the torrance test. Journal of Creativity, 33 (3), Article 100065. https://doi.org/10.1016/j. yjoc.2023.100065 · DOI
  21. Holford, W. D. (2019). The future of human creative knowledge work within the digital economy. Futures, 105 , 143 -154. 10.1016/j.futures.2018.10.002. · DOI
  22. Karwowski, M. (2015). Notes on creative potential and its measurement. Creativity. Theories Research Applications, 2 (1), 4 -16. https://doi.org/10.1515/ctra-2015-0001 · DOI
  23. Kaufman, J. C., Beghetto, R. A., & Watson, C. (2016). Creative metacognition and selfratings of creative performance: A 4-C perspective. Learning and Individual Differences, 51 , 394 -399. https://doi.org/10.1016/j.lindif.2015.05.004 · DOI
  24. Kirkpatrick, K. (2023). Can AI demonstrate creativity? Communications of the ACM, 66 (2), 21 -23.
  25. Lazar, M., Miron-Spektor, E., & Mueller, J. S. (2022). Love at first insight: An attachment perspective on early-phase idea selection. Organizational Behavior and Human Decision Processes, 172 , Article 104168. https://doi.org/10.1016/j. obhdp.2022.104168 · DOI
  26. Luria, S. R., & Kaufman, J. C. (2017). The dynamic force before intrinsic motivation: Exploring creative needs. The creative self (pp. 317 -325). Elsevier. http://refhub.elsevier.com/S2713-3745(2300025-0/sbref0031)
  27. Maiden, N. (2019, June). Digital creativity support: designing AI to augment human creativity. In The Human Position in an Artificial World (pp. 145 -146). Creativity, Ethics and AI in Knowledge Organization. http://refhub.elsevier.com/S2713-3745(2300025-0/sbref0032)
  28. Miller, A. I. (2019). The artist in the machine: the world of AI-powered creativity . MIT Press. Much to discuss in AI ethics. Nature Machine Intelligence, 4 (12), (2022). https://doi.org/ 10.1038/s42256-022-00598-x · DOI
  29. Müller-Wienbergen, F., Müller, O., Seidel, S., & Becker. (2011). Leaving the beaten tracks in creative work -A design theory for systems that support convergent and divergent thinking. Journal of the Association for Information Systems, 12 (11). https://doi.org/ 10.17705/1jais.00280 · DOI
  30. Marcus, G., Davis, E., & Aaronson, S. (2022). A very preliminary analysis of DALL-E 2. arXiv preprint arXiv:2204.13807. · arXiv
  31. OpenAI. (2023a). ChatGPT-4 (Mar 14 version) [Large language model]. OpenAI. (2023b). GPT-4 Technical Report arXiv. 10.48550/arXiv.2303.08774. · DOI · Paper page
  32. Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2008). Situation awareness, mental workload, and trust in automation: Viable, empirically supported cognitive engineering constructs. Journal of Cognitive Engineering and Decision Making, 2 (2), 140 -160. https://doi.org/10.1518/155534308X284417 · DOI
  33. Pistrui, J. (2018). The future of human work is imagination, creativity, and strategy. Harvard Business Review . http://refhub.elsevier.com/S2713-3745(2300025-0/sbref0042)
  34. Plucker, J. A., Beghetto, R. A., & Dow, G. T. (2004). Why isn ' t creativity more important to educational psychologists? potentials, pitfalls, and future directions in creativity research. Educational Psychologist, 39 (2), 83 -96. https://doi.org/10.1207/ s15326985ep3902\_1
  35. Reddy, A. (2022). Artificial everyday creativity: Creative leaps with AI through critical making. Digital Creativity, 33 (4), 295 -313. https://doi.org/10.1080/ 14626268.2022.2138452
  36. Reiter-Palmon, R., Forthmann, B., & Barbot, B. (2019). Scoring divergent thinking tests: A review and systematic framework. Psychology of Aesthetics, Creativity, and the Arts, 13 (2), 144. http://refhub.elsevier.com/S2713-3745(2300025-0/sbref0045)
  37. Richards, R., Kaufman, J. C., & Sternberg, R. J. (2010). Everyday creativity: process and way of life -Four key issues. The cambridge handbook of creativity (pp. 189 -215). Cambridge University Press. https://doi.org/10.1017/CBO9780511763205.013 · DOI
  38. Runco, M. A. (2023). AI can only produce artificial creativity. Journal of Creativity . , Article 100063. https://doi.org/10.1016/j.yjoc.2023.100063 · DOI
  39. Runco, M. A., Abdulla, A. M., Paek, S. H., Al-Jasim, F. A., & Alsuwaidi, H. N. (2016). Which test of divergent thinking is best? Creativity Theories Research Applications, 3 (1), 4 -18. https://doi.org/10.1515/ctra-2016-0001 · DOI
  40. Sawyer, K. (2012). Extending sociocultural theory to group creativity. Vocations and Learning, 5 (1), 59 -75. http://refhub.elsevier.com/S2713-3745(2300025-0/sbref0050)
  41. Seidel, S., & Berente, N. (2020). Automate, informate, and generate: Affordance primitives of smart devices and the Internet of Things. Handbook of Digital Innovation , 198 -210. http://refhub.elsevier.com/S2713-3745(2300025-0/sbref0051)
  42. Silvia, P. J., Winterstein, B. P., Willse, J. T., Barona, C. M., Cram, J. T., Hess, K. I., Martinez, J. L., & Richard, C. A. (2008). Assessing creativity with divergent thinking tasks: Exploring the reliability and validity of new subjective scoring methods. Psychology of Aesthetics, Creativity and the Arts, 2 (2), 68 -85. http://refhub.elsevier.com/S2713-3745(2300025-0/sbref0052)
  43. Simonton, D. K. (2013). What is a creative idea? Little-c versus Big-C creativity. Handbook of Research on Creativity, 2 , 69 -83. http://refhub.elsevier.com/S2713-3745(2300025-0/sbref0053)
  44. Steele, L. M., Johnson, G., & Medeiros, K. E. (2018). Looking beyond the generation of creative ideas: Confidence in evaluating ideas predicts creative outcomes. Personality and Individual Differences, 125 , 21 -29. https://doi.org/10.1016/j.paid.2017.12.028 · DOI
  45. The AI writing on the wall. Nature Machine Intelligence, 5 (1), (2023). https://doi.org/ 10.1038/s42256-023-00613-9 · DOI
  46. Vartanian, O., Beatty, E. L., Smith, I., Forbes, S., Rice, E., & Crocker, J. (2019). Measurement matters: The relationship between methods of scoring the alternate uses task and brain activation. Current Opinion in Behavioral Sciences, 27 , 109 -115. https://doi.org/10.1016/j.cobeha.2018.10.012 · DOI
  47. Verganti, R., Vendraminelli, L., & Iansiti, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37 (3), 212 -227. https://doi.org/10.1111/jpim.12523 · DOI · Paper page
  48. Vinchon, F., Lubart, T., Bartolotta, S., Gironnay, V., Botella, M., Bourgeois-Bougrine, S., … Gaggioli, A. (2023). Artificial Intelligence & Creativity: A Manifesto for Collaboration. The Journal of Creative Behavior, 597 . 10.1002/jocb.597. · DOI · Paper page
  49. Williams, R., Runco, M., & Berlow, E. (2016). Mapping the themes, impact, and cohesion of creativity research over the last 25 years. Creativity Research Journal, 28 , 385 -394. https://doi.org/10.1080/10400419.2016.1230358 · DOI

Authors
Haase, J., & Hanel, P. H. P.
Affiliations
  • Humboldt-Universität zu Berlin (DE)
  • University of Essex (GB)
Corresponding author
Haase, Jennifer
ORCID
Venue
Journal of Creativity
Publisher
Elsevier BV
Published
2023-12
Volume
33
Issue
3
Pages
100066
ISSN
2713-3745 (ISSN-L)
DOI
10.1016/j.yjoc.2023.100066
OpenAlex
W4387453630
Cited by
233
References
50
Citations by year
2026: 64 · 2025: 100 · 2024: 54 · 2023: 15
FWCI
56.29
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Creativity (publishedVersion, cc-by)
  • Open Access at Essex (University of Essex) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • OSF Preprints (OSF Preprints) (submittedVersion)
Funders
Bundesministerium für Bildung und Forschung (16DII133)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Creativity in Education and Neuroscience
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
Keywords
generative AI, chatbots, creativity, human-AI comparison, idea generation, GPT-4, future of creativity

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review