Shuichiro Ogawa
日本語

Notes · updated 2026-08-02

The Scholarly Lineage of Generative Art: A 94-Item Literature Map from Information Aesthetics to the Post-LLM Era

Scope and Method

This note is a literature map that organizes scholarly research on generative art (art practice that delegates part or all of production to an autonomous system) into four streams sufficient for the chapter structure of a review article.

  • Collection was carried out in mode: academic (source-collection skill), yielding 94 confirmed items from 108 explored rows after merging 8 duplicates (0 excluded). Corpus: source/review/generative-art/papers.md.
  • The four streams are: H = historical origins (24 items), T = technical genealogy (21 items after merging), F = theoretical frameworks (26 items), and L = the post-LLM era (29 items).
  • The covered period is 1958–2026, with the L stream weighted toward 2024–2026.
  • Primary sources from the pre-DOI era (1960s catalogs, dissertations) are retained as historical primary sources with flag: non-peer.

The industry side (markets, tools, practitioner testimony) is covered by generative-art-ecosystem-industry. This note is limited to scholarly literature. An adversarial review of this note’s genealogical structure and gap claims is in generative-art-adversarial-review. Its use as educational material (a 72-item literature map across seven purpose types) is covered by generative-art-education-literature.

Chapter 1: Historical Origins, 1960s Computer Art and Information Aesthetics

The term generative art emerged in a theoretical context far older than deep learning.

The theoretical foundation is information aesthetics (Informationsästhetik, the program of treating aesthetic objects quantitatively with Shannon’s information theory). Moles (1958/1966) connected information theory to aesthetic perception, and Max Bense’s Stuttgart School turned it into a guide for production. In issue 19 of the magazine “rot,” published for Georg Nees’s February 1965 exhibition (the world’s first exhibition of algorithmic art), Bense used the term generative Ästhetik (generative aesthetics) for the first time (H21). Nees’s doctoral dissertation (1969, H03) implemented this aesthetics as plotter-drawing algorithms, the field’s first dissertation.

The early history of practice rests on two pillars: Nake’s first-person recollection (2005, H07) and Noll’s subject experiment (1966, H01). In Noll’s experiment, only 28% of 100 subjects correctly identified the computer-generated picture, and 59% preferred the computer version to Mondrian’s original. The question of aesthetic evaluation of machine-generated artifacts, the topic of later reception studies (Chapter 3), is already present here.

1968 was a branching point. London’s Cybernetic Serendipity (H17; historiographic analysis in H04) and Zagreb’s New Tendencies (H09) opened in the same summer, and Klütsch (2005, H06) contrasts the former as a display of possibilities and the latter as visual research charged with social and political implications. The subsequent reception was not smooth. Taylor (2014, H08) traces critical discourse from 1963 to 1989 and argues that the art world’s rejection was motivated less by aesthetic judgment than by fear of the machine. The fact that Nake himself wrote “There Should Be No Computer Art” in 1971 (H19) shows that tensions rooted in the critique of capitalism existed inside the movement as well.

The reference point for the definitional debate is Galanter. His 2003 definition, that generative art is an art practice in which the artist cedes part of production to a system that behaves autonomously (H10), brought complexity theory into the aesthetic context and was systematized in the 2016 book chapter (H11). Harold Cohen’s AARON, the longest-running practice of autonomous rule-based AI drawing, is documented by McCorduck (1990, H22).

Chapter 2: Technical Genealogy, from Procedural Generation to Diffusion Models and LLMs

The technical genealogy reads as four generations.

The first generation is procedural generation. L-systems (Prusinkiewicz & Lindenmayer 1990, T-A01) systematized grammatical growth models and established the idea of generating form from formal rules.

The second generation is evolutionary art. Sims demonstrated generation under human selection pressure with genetic programming for images (1991, T-A02) and the co-evolution of morphology and neural control (1994, T-B01). Interactive Evolutionary Computation, which places human sensibility in the fitness function, was surveyed across roughly 250 papers by Takagi (2001, T-B02). McCormack’s ten open problems (2005, T-B03), including automated aesthetic evaluation and fitness-function design, articulated this generation’s limits, and the Romero & Machado handbook (2008, T-B04) became the standard reference. As a later verification, McCormack & Cruz Gambardella (2022, T-B05) measured correlations between multiple complexity measures and aesthetic judgment and reported that no universal aesthetic measure exists.

The third generation is deep generative models. VAE (T-D01) and GAN (T-D02) built the foundations, and neural style transfer (Gatys et al. 2016, T-E02) demonstrated the separation and recombination of style. The art-specific turn is CAN (Elgammal et al. 2017, T-E03), which modified the GAN objective to approach existing styles while deviating from style norms, reporting that subjects could not distinguish its output from human contemporary art. StyleGAN (2019, T-E04) raised controllable generation to a practical level.

The fourth generation is diffusion models and text-to-image. DDPM (2020, T-F01) demonstrated the quality of diffusion probabilistic models, CLIP (2021, T-F02) provided the semantic bridge between language and images, and DALL-E (T-F03), DALL-E 2 (T-F05), and Latent Diffusion (2022, T-F04, the basis of Stable Diffusion) generalized “drawing with text.” The movement toward LLMs as a control layer for multimodal generation is organized in a survey (T-G01).

As a through-line across the genealogy, Broad et al. (2021, T-H01) identified the problem that deep generative models are bound to their training distribution and taxonomized methods for actively diverging from it. This generalizes CAN’s concern (deviation from style norms) and connects to the creativity theory of Chapter 3 (Boden’s transformational creativity).

Chapter 3: Theoretical Frameworks, Creativity, Authorship, Aesthetics

The starting point of theory is Boden’s three types. Combinational, exploratory, and transformational creativity (new combinations of existing elements, search within a conceptual space, and transformation of the space itself) were formulated in The Creative Mind (1990/2004, F01/F03), and their realizability in AI was argued in a journal article (1998, F02).

The computational creativity community developed this framework into formalization and evaluation. Wiggins (2006, F06) turned Boden’s description into a formally comparable model, Ritchie (2007, F10) proposed empirical criteria for attributing creativity from output, and Jordanous contributed the SPECS evaluation procedure (2012, F08) and the four-P organization (2016, F09). Colton’s Creative Tripod (2008, F12) argued that the perception of skill, imagination, and appreciation, not the artifact alone, governs creativity attribution, and Lamb et al. (2018, F13) organized evaluation methods across disciplines. As a criterion for machine creativity, the Lovelace test, which demands output the system’s designers cannot explain (Bringsjord et al. 2001, F14), stands as the opposing axis.

The authorship debate reads as two poles. Hertzmann (2018, T-C04; 2020, F16) argues from the precedent of photography that computers are tools and the author is the human social agent. By contrast, McCormack and colleagues conceptualize the distribution of authorship according to the degree of delegation, in the ten questions on generative art (2014, F17) and the re-examination of autonomy, authenticity, authorship, and intention (2019, F18). Galanter (2019, F20) identified the tension that deep-learning generation is not fully explained by the older complexity-based theory. As a recent philosophical response, Nannicelli (2025, F29) named Midjourney-type output mass AI-art and formulated a moderate skepticism that denies art status where sufficient intentional human control is absent.

Empirical reception studies repeatedly confirm the label effect. Chamberlain et al. (2018, F23), Hong & Curran (2019, F27), Ragot et al. (2020, F24), Gangadharbatla (2022, F26), and Bellaiche et al. (2023, F25) show that the same work is rated lower aesthetically, and attracts lower purchase intent, when labeled AI-generated, and that identification without labels is itself difficult. Placed beside Noll’s 1966 experiment (H01), the structure, that machine-generated works cannot be identified yet are devalued once known to be machine-made, has remained largely unchanged for sixty years.

Chapter 4: The Post-LLM Context, 2024–2026

Following the request’s emphasis, this chapter is the thickest. The 29 items are organized into five bundles.

Text-to-Image Practice and Reception

As large-scale evidence, Zhou & Lee (2024, L01) used difference-in-differences estimation over roughly four million works and reported that after text-to-image adoption, creative productivity rose 25% and work value (favorites per view) rose 50%, while average content novelty declined and peak novelty increased. Even as quantity and value grow, the center of the distribution converges. Among qualitative studies of practice, Rajcic et al. (2024, L03) gave seven artists a Stable Diffusion model fine-tuned on their own work for two weeks, finding two dominant modes of use (ideation and production) and reports of lost contact with physical materials. Mim et al. (2024, L02) documented effects on local creative fields in the Global South through a five-month ethnography of image practitioners in Dhaka. On the reception side, Malecki et al. (2025, L05) confirmed lower aesthetic evaluation under AI attribution in an experiment with n=470, continuing the label-effect lineage of Chapter 3 into the post-LLM era.

LLM Code Generation and Creative Coding

This bundle carries the connection between the generative-art tradition (procedural generation in Chapter 2) and LLMs. Spellburst (2023, L06), an LLM-driven node-based environment, bridged the gap between natural-language prompts and code editing. GenP5 and P52Style by Wu & Adar (2024, L07) integrate p5.js algorithmic art and diffusion models bidirectionally, one of the few studies that explicitly connects the older lineage with the new technology. In education, Flowcode (2026, L09) supports novices’ productive AI use through visualization and deliberate friction, and fog (2026, L10) verified expression through function composition of AI-generated code by perceptual evaluation. In the Research Through Design line, a tension is reported in which sketching with LLMs forces premature explicitness on fuzzy phenomena (2026, L11).

Human-AI Co-Creation and Prompting as Skill

Shelby et al. (2024, L12) extracted folk theories of text-to-image use, harm, and harm reduction from fifteen artists in ten countries. Noise Pilot (2026, L13) made the diffusion model’s noise estimates directly manipulable, demonstrating deep engagement that reaches results unattainable by prompting alone. Oppenlaender et al. (2024, L16) positioned prompt engineering as a learnable creative skill through a three-stage experiment. A cross-level analysis in the design context (Naqvi et al. 2025, L14) shows acceptance as a collaborative tool coexisting with disputes over originality and ownership.

Protection technology has taken on the character of an arms race. Glaze (2023, L17) obstructs style mimicry with imperceptible perturbations (reported success rate >92%), and Nightshade (2024, L18) advanced to model poisoning with small numbers of samples. Against these, Hönig et al. (2024, L19) demonstrated that such protections can be bypassed by simple image processing and provide only a false sense of security; the conflict remains unresolved. The distribution of stakeholder opinion is shown by Lovato et al.’s survey of 459 artists (2024, L20): support for training-data disclosure, attribution, and fair distribution. On the legal side, Lemley (2024, L21) argues that generative AI upends copyright’s idea-expression dichotomy, and Buick (2024, L22) evaluates the EU AI Act’s transparency provisions as necessary but insufficient. Porquet et al.’s illustrator study (2025, L23) points to the paradox that style transfer succeeds at copying aesthetic fragments while lacking emergent quality, yet the substitution threat can still materialize.

Labor and Social Impact

Empirical results are split. Makridis’s panel estimation (2026, L25) finds no evidence of short-term income decline in artistic occupations with high LLM exposure. By contrast, the five-year longitudinal study of seventeen Chinese digital painters by Meng et al. (2025, L26) includes reports of pay cuts and job loss, and records attitudes shifting from resistance through pragmatic adoption to reflective reconstruction. At the level of discourse, Bender (2024, L24) critiques the task-substitution model using the 2023 writers’ and actors’ strikes, and Caramiaux et al. (2025, L27) analyze the structure of the dominant narrative of creativity freed from the material realization of labor. For overviews of the research landscape, the 57-paper scoping review by Tsao et al. (2025, L28) and the systematic review of 189 HCI papers by Hu et al. (2025, L29) jointly identify the shift toward curation, prompting as a new literacy, and the rearrangement of expertise hierarchies.

Gaps (Unmet Questions)

The gaps a review article could target, as read from the corpus:

  • Disconnection between theory and empirical work: the evaluation frameworks of computational creativity (SPECS, the Creative Tripod, the Lovelace test) are almost never cited in post-LLM empirical research (the L stream). No study in this corpus examines diffusion or LLM outputs against Boden’s types or the F-stream evaluation theory.
  • Weak connection between history and the present: research that theoretically connects 1960s information aesthetics (the quantitative program of generative aesthetics) with modern generative-model research is thin, apart from exceptions such as Wu & Adar (L07). No literature was found that explicitly argues the continuity between the sixty-year-old question of identifying and evaluating machine-generated works (H01) and current label-effect studies (F24, L05).
  • Unresolved protection technology: the conflict between the Glaze/Nightshade line and bypass attacks (L19) is technically unresolved, and research addressing the normative question (what artists should rely on) is lacking.
  • Divergent labor measurements: short-term panels (L25) and long-term longitudinal studies (L26) reach different conclusions, and no comparative study disentangles measurement period, occupational definitions, and region.
  • Regional and jurisdictional bias: legal scholarship is skewed toward US and EU law; academic literature on Japan’s copyright revision (Article 30-4) is not included. Non-Western practice studies are limited to L02 (Bangladesh) and L26 (China).
  • Environmental cost: no peer-reviewed study specific to the environmental cost of generative art was identified.

Unverified Items

The corpus carries 18 [要一次検証] (primary-verification-needed) markers, all concerning bibliographic confirmation rather than suspected fabrication of claims.

  1. H03: formal bibliography of the original Nees 1969 (Siemens Verlag edition)
  2. H09: DOI for Rosen 2011 (MIT Press)
  3. H10: official archive for Galanter 2003 (only the author’s site PDF confirmed)
  4. H12: DOI for Brown et al. 2008 (MIT Press)
  5. H17: Reichardt 1968 (no DOI; reachable via Internet Archive)
  6. H18: Reichardt 1971 (same)
  7. H19: Nake 1971 (PAGE bulletin, no DOI)
  8. H20: Moles 1966 (no DOI, ISBN only)
  9. H21: Nees/Nake/Bense 1965 “rot” No. 19 (no DOI)
  10. H22: McCorduck 1990 (no DOI)
  11. F01: Boden 1990 first edition (no DOI, ISBN only)
  12. F03: Boden 2004 second edition (same)
  13. F05: Boden 2010 (same)
  14. F12: permanent reachability of the AAAI PDF for Colton 2008
  15. T-H01: DOI of the peer-reviewed ICCC 2021 version of Broad et al. 2021
  16. L07: existence of the Springer chapter DOI (10.1007/978-3-031-90167-6_15) for Wu & Adar
  17. L19: formal ICLR 2025 proceedings DOI for Hönig et al.
  18. L25: full text of Makridis 2026 (abstract only; Springer authentication wall)

References

All accessed 2026-08-02. Ids correspond to rows in the corpus source/review/generative-art/papers.md.

Chapter 1 (Historical Origins)

  1. (H01) Noll, A. M. 1966. Human or Machine: A Subjective Comparison of Piet Mondrian’s “Composition with Lines” and a Computer-Generated Picture. The Psychological Record 16. https://doi.org/10.1007/BF03393635
  2. (H02) Noll, A. M. 1967. The Digital Computer as a Creative Medium. IEEE Spectrum 4(10). https://doi.org/10.1109/MSPEC.1967.5217127
  3. (H03) Nees, G. 1969. Generative Computergraphik. Siemens Verlag. https://archive.org/details/generative_computergraphik
  4. (H04) Usselmann, R. 2003. The Dilemma of Media Art: Cybernetic Serendipity at the ICA London. Leonardo 36(5). https://doi.org/10.1162/002409403771048191
  5. (H05) Klütsch, C. 2007. Computer Graphic—Aesthetic Experiments between Two Cultures. Leonardo 40(5). https://doi.org/10.1162/leon.2007.40.5.421
  6. (H06) Klütsch, C. 2005. The Summer 1968 in London and Zagreb. Proceedings of Creativity & Cognition 2005. https://doi.org/10.1145/1056224.1056241
  7. (H07) Nake, F. 2005. Computer Art: A Personal Recollection. Proceedings of Creativity & Cognition 2005. https://doi.org/10.1145/1056224.1056234
  8. (H08) Taylor, G. D. 2014. When the Machine Made Art: The Troubled History of Computer Art. Bloomsbury Academic. https://doi.org/10.5040/9781628929980
  9. (H09) Rosen, M. (ed.) 2011. A Little-Known Story about a Movement, a Magazine, and the Computer’s Arrival in Art. MIT Press. https://mitpress.mit.edu/9780262515818/
  10. (H10) Galanter, P. 2003. What is Generative Art? Complexity Theory as a Context for Art Theory. GA2003. https://www.philipgalanter.com/downloads/ga2003_paper.pdf
  11. (H11) Galanter, P. 2016. Generative Art Theory. In A Companion to Digital Art. Wiley. https://doi.org/10.1002/9781118475249.ch5
  12. (H12) Brown, P.; Gere, C.; Lambert, N.; Mason, C. (eds.) 2008. White Heat Cold Logic: British Computer Art 1960–1980. MIT Press. https://mitpress.mit.edu/9780262026536/
  13. (H13) Higgins, H.; Kahn, D. (eds.) 2012. Mainframe Experimentalism. UC Press. https://www.ucpress.edu/books/mainframe-experimentalism
  14. (H14) Guillermet, A. 2020. Vera Molnar’s Computer Paintings. Representations 149(1). https://doi.org/10.1525/rep.2020.149.1.1
  15. (H15) Franceschet, M. 2025. Modern Forms of Generative Art. Leonardo 58(4). https://doi.org/10.1162/LEON.a.77
  16. (H16) Klütsch, C. 2012. Information Aesthetics and the Stuttgart School. In Mainframe Experimentalism. UC Press. https://doi.org/10.1525/9780520953734-007
  17. (H17) Reichardt, J. (ed.) 1968. Cybernetic Serendipity: The Computer and the Arts. Studio International. https://archive.org/details/cybernetic-serendipity
  18. (H18) Reichardt, J. (ed.) 1971. Cybernetics, Art and Ideas. Studio Vista. https://archive.org/details/cyberneticsartid0000reic
  19. (H19) Nake, F. 1971. There Should Be No Computer Art. PAGE 18. https://dam.org/museum/wp-content/uploads/2021/05/Nake1971-there-should-be-no-computer-art.pdf
  20. (H20) Moles, A. A. 1966. Information Theory and Esthetic Perception. University of Illinois Press. ISBN 978-0-252-72485-5
  21. (H21) Nees, G.; Nake, F.; Bense, M. 1965. Computer-Grafik (“rot” No.19). Edition Hansjörg Mayer. http://dada.compart-bremen.de/item/publication/339
  22. (H22) McCorduck, P. 1990. Aaron’s Code. W. H. Freeman. https://archive.org/details/aaronscodemetaar0000mcco
  23. (H23) Franceschet, M. et al. 2021. Crypto Art: A Decentralized View. Leonardo 54(4). https://doi.org/10.1162/leon_a_02003
  24. (H24) Mazzone, M.; Elgammal, A. 2019. Art, Creativity, and the Potential of Artificial Intelligence. Arts 8(1). https://doi.org/10.3390/arts8010026

Chapter 2 (Technical Genealogy)

  1. (T-A01) Prusinkiewicz, P.; Lindenmayer, A. 1990. The Algorithmic Beauty of Plants. Springer. https://doi.org/10.1007/978-1-4613-8476-2
  2. (T-A02) Sims, K. 1991. Artificial Evolution for Computer Graphics. SIGGRAPH ‘91. https://dl.acm.org/doi/10.1145/127719.122752
  3. (T-B01) Sims, K. 1994. Evolving Virtual Creatures. SIGGRAPH ‘94. https://dl.acm.org/doi/10.1145/192161.192167
  4. (T-B02) Takagi, H. 2001. Interactive Evolutionary Computation. Proceedings of the IEEE 89(9). https://doi.org/10.1109/5.949485
  5. (T-B03) McCormack, J. 2005. Open Problems in Evolutionary Music and Art. LNCS 3449. https://doi.org/10.1007/978-3-540-32003-6_43
  6. (T-B04) Romero, J.; Machado, P. (eds.) 2008. The Art of Artificial Evolution. Springer. https://doi.org/10.1007/978-3-540-72877-1
  7. (T-B05) McCormack, J.; Cruz Gambardella, C. 2022. Complexity and Aesthetics in Generative and Evolutionary Art. Genetic Programming and Evolvable Machines 23. https://doi.org/10.1007/s10710-022-09429-9
  8. (T-C04) Hertzmann, A. 2018. Can Computers Create Art? Arts 7(2). https://doi.org/10.3390/arts7020018
  9. (T-D01) Kingma, D. P.; Welling, M. 2013. Auto-Encoding Variational Bayes. ICLR 2014. https://arxiv.org/abs/1312.6114
  10. (T-D02) Goodfellow, I. et al. 2014. Generative Adversarial Nets. NeurIPS 2014. https://dl.acm.org/doi/10.5555/2969033.2969125
  11. (T-E02) Gatys, L. A.; Ecker, A. S.; Bethge, M. 2016. Image Style Transfer Using Convolutional Neural Networks. CVPR 2016. https://doi.org/10.1109/CVPR.2016.265
  12. (T-E03) Elgammal, A. et al. 2017. CAN: Creative Adversarial Networks. ICCC 2017. https://arxiv.org/abs/1706.07068
  13. (T-E04) Karras, T.; Laine, S.; Aila, T. 2019. A Style-Based Generator Architecture for GANs. CVPR 2019. https://doi.org/10.1109/CVPR.2019.00453
  14. (T-F01) Ho, J.; Jain, A.; Abbeel, P. 2020. Denoising Diffusion Probabilistic Models. NeurIPS 2020. https://arxiv.org/abs/2006.11239
  15. (T-F02) Radford, A. et al. 2021. Learning Transferable Visual Models From Natural Language Supervision. ICML 2021. https://proceedings.mlr.press/v139/radford21a.html
  16. (T-F03) Ramesh, A. et al. 2021. Zero-Shot Text-to-Image Generation. ICML 2021. https://arxiv.org/abs/2102.12092
  17. (T-F04) Rombach, R. et al. 2022. High-Resolution Image Synthesis with Latent Diffusion Models. CVPR 2022. https://doi.org/10.1109/CVPR52688.2022.01042
  18. (T-F05) Ramesh, A. et al. 2022. Hierarchical Text-Conditional Image Generation with CLIP Latents. arXiv. https://arxiv.org/abs/2204.06125
  19. (T-G01) He, Y. et al. 2024. LLMs Meet Multimodal Generation and Editing: A Survey. arXiv. https://arxiv.org/abs/2405.19334
  20. (T-H01) Broad, T. et al. 2021. Active Divergence with Generative Deep Learning. arXiv/ICCC 2021. https://arxiv.org/abs/2107.05599
  21. (T-H02) Maerten, A.-S.; Soydaner, D. 2023. From Paintbrush to Pixel. arXiv. https://arxiv.org/abs/2302.10913

Chapter 3 (Theoretical Frameworks)

  1. (F01) Boden, M. A. 1990. The Creative Mind: Myths and Mechanisms (1st ed.). Weidenfeld & Nicolson. ISBN 9780747411505
  2. (F02) Boden, M. A. 1998. Creativity and Artificial Intelligence. Artificial Intelligence 103. https://doi.org/10.1016/S0004-3702(98)00055-1
  3. (F03) Boden, M. A. 2004. The Creative Mind (2nd ed.). Routledge. ISBN 9780415314534
  4. (F04) Boden, M. A. 2009. Computer Models of Creativity. AI Magazine 30(3). https://doi.org/10.1609/aimag.v30i3.2254
  5. (F05) Boden, M. A. 2010. Creativity and Art: Three Roads to Surprise. Oxford University Press. ISBN 9780199590735
  6. (F06) Wiggins, G. A. 2006. A Preliminary Framework for Description, Analysis and Comparison of Creative Systems. Knowledge-Based Systems 19(7). https://doi.org/10.1016/j.knosys.2006.04.009
  7. (F07) Wiggins, G. A. 2012. The Mind’s Chorus. Cognitive Computation 4. https://doi.org/10.1007/s12559-012-9151-6
  8. (F08) Jordanous, A. 2012. A Standardised Procedure for Evaluating Creative Systems. Cognitive Computation 4(3). https://doi.org/10.1007/s12559-012-9156-1
  9. (F09) Jordanous, A. 2016. Four PPPPerspectives on Computational Creativity. Connection Science 28(2). https://doi.org/10.1080/09540091.2016.1151860
  10. (F10) Ritchie, G. 2007. Some Empirical Criteria for Attributing Creativity to a Computer Program. Minds and Machines 17(1). https://doi.org/10.1007/s11023-007-9066-2
  11. (F11) Colton, S.; Wiggins, G. A. 2012. Computational Creativity: The Final Frontier? ECAI 2012. https://doi.org/10.3233/978-1-61499-098-7-21
  12. (F12) Colton, S. 2008. Creativity Versus the Perception of Creativity in Computational Systems. AAAI Spring Symposium. https://cdn.aaai.org/Symposia/Spring/2008/SS-08-03/SS08-03-003.pdf
  13. (F13) Lamb, C.; Brown, D. G.; Clarke, C. L. A. 2018. Evaluating Computational Creativity. ACM Computing Surveys 51(2). https://doi.org/10.1145/3167476
  14. (F14) Bringsjord, S.; Bello, P.; Ferrucci, D. 2001. Creativity, the Turing Test, and the (Better) Lovelace Test. Minds and Machines 11. https://doi.org/10.1023/A:1011206622741
  15. (F16) Hertzmann, A. 2020. Computers Do Not Make Art, People Do. Communications of the ACM 63. https://doi.org/10.1145/3347092
  16. (F17) McCormack, J. et al. 2014. Ten Questions Concerning Generative Computer Art. Leonardo 47(2). https://doi.org/10.1162/LEON_a_00533
  17. (F18) McCormack, J.; Gifford, T.; Hutchings, P. 2019. Autonomy, Authenticity, Authorship and Intention in Computer Generated Art. LNCS 11453. https://doi.org/10.1007/978-3-030-16667-0_3
  18. (F20) Galanter, P. 2019. Artificial Intelligence and Problems in Generative Art Theory. EVA London 2019. https://doi.org/10.14236/ewic/EVA2019.22
  19. (F23) Chamberlain, R. et al. 2018. Putting the Art in Artificial. Psychology of Aesthetics, Creativity, and the Arts 12(2). https://doi.org/10.1037/aca0000136
  20. (F24) Ragot, M.; Martin, N.; Cojean, S. 2020. AI-Generated vs. Human Artworks. CHI 2020 EA. https://doi.org/10.1145/3334480.3382892
  21. (F25) Bellaiche, L. et al. 2023. Humans Versus AI. Cognitive Research: Principles and Implications 8. https://doi.org/10.1186/s41235-023-00499-6
  22. (F26) Gangadharbatla, H. 2022. The Role of AI Attribution Knowledge in the Evaluation of Artwork. Empirical Studies of the Arts 40. https://doi.org/10.1177/0276237421994697
  23. (F27) Hong, J.-W.; Curran, N. 2019. Artificial Intelligence, Artists, and Art. ACM TOMM 15(2s). https://doi.org/10.1145/3326337
  24. (F28) Gaut, B. 2010. The Philosophy of Creativity. Philosophy Compass 5(12). https://doi.org/10.1111/j.1747-9991.2010.00351.x
  25. (F29) Nannicelli, T. 2025. Mass AI-art: A Moderately Skeptical Perspective. Journal of Aesthetics and Art Criticism 83(4). https://doi.org/10.1093/jaac/kpaf026
  26. (F30) Gunkel, D. J. 2017. Rethinking Art and Aesthetics in the Age of Creative Machines. Philosophy & Technology 30(3). https://doi.org/10.1007/s13347-017-0281-3

Chapter 4 (Post-LLM)

  1. (L01) Zhou, E. B.; Lee, D. 2024. Generative artificial intelligence, human creativity, and art. PNAS Nexus 3(3). https://doi.org/10.1093/pnasnexus/pgae052
  2. (L02) Mim, N. J. et al. 2024. In-Between Visuals and Visible. CHI 2024. https://doi.org/10.1145/3613904.3641951
  3. (L03) Rajcic, N.; Llano, M. T.; McCormack, J. 2024. Towards A Diffractive Analysis of Prompt-Based Generative AI. CHI 2024. https://doi.org/10.1145/3613904.3641971
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