Preparing a Review Paper on the Symbiosis of AI and Design: The Lineage of Symbiosis, Co-Creation Frameworks, Design-Specific Evidence, Philosophical Foundations, and a Map of Existing Reviews

This note is a preparatory record of what is in place, and what is missing, for writing the text of a review paper.

What the review asks

The phrase “the symbiosis of AI and design” appears both in research on co-creation in the 2020s and in visions of the computer from the 1960s. Although the same word is used sixty years apart, research from the 2020s rarely cites the visions of the 1960s (see the examination of reference lists below). The question of the review is as follows: when research on human-AI co-creation from 2017 to 2026 is placed again on the currents of the philosophy of design (the six currents in Essential Readings for Researching the Philosophy of Design in the Age of AI (A Guide by Current)) and on the lineage of the term symbiosis, what is separated from a shared shelf, and what is connected across different shelves?

The provisional position is as follows. Existing reviews arrange the literature by type of tool, stage of design, type of interaction, and allocation of agency. In that arrangement, “AI as a tool” and “AI as a material” go on the same shelf (support for ideation), while “a critique of design from the philosophy of AI” (Winograd and Flores 1986) and “the difficulty of designing AI” (Yang et al. 2020) go on different shelves (philosophy of AI and HCI). If the literature is rearranged by the questions of the philosophy of design, the former pair is separated and the latter pair is connected. Whether this rearrangement holds is the substance of the review’s novelty on the RW axis.

Symbiosis, augmentation, integration: the lineage of the terms (1960 to 1976)

The term symbiosis was introduced by Licklider (1960). Licklider called the state in which humans and computers are tightly coupled in cooperative interaction a symbiosis, and he named two aims. The first was for the computer to facilitate formulative thinking, not only the solution of formulated problems. The second was for humans and computers to cooperate in making decisions and controlling complex situations without inflexible dependence on predetermined programs. Licklider distinguished this vision from North’s “mechanically extended man,” in which the human supplies all of the initiative, direction, integration, and criteria. He also distinguished it from artificial intelligence: granting that machines might eventually take over the main role in thinking, he held that a long interim would be carried by close cooperation between humans and computers. The fig tree and the fig wasp served as the metaphor, and the motivation was his observation that about 85 percent of his “thinking” time was spent getting into a position to think (full text).

The term augmentation was introduced by Engelbart (1962). Engelbart defined “augmenting human intellect” as increasing the capability of a person to approach a complex problem situation, to gain comprehension, and to derive solutions, and he attributed that capability to a system of humans, language, artifacts, methodology, and training (H-LAM/T). The computer belongs to the artifact side of this system. Engelbart cites Licklider as the person who coined symbiosis as a term for a relationship of close interaction within mutually beneficial cooperation (full text).

On the design side, Negroponte (1970) was the first to take symbiosis as a theme. The publisher’s description states that the book treats design as a process of reasoning and calls for personalized computer systems that can handle ambiguity, inconsistency, and the whims peculiar to design thinking. The Crossref table of contents includes a chapter titled “Architect-Machine Symbiosis” (publisher’s description and table of contents; the text has not been read). For Negroponte (1976), only the table of contents was confirmed. Steenson (2017), a historical study that traces Alexander, Wurman, Price, Negroponte, and the Architecture Machine Group at MIT, is a close precedent that surveys this lineage in a single book (bookseller’s catalog and Crossref table of contents; the text has not been read).

The point of contact between architecture and cybernetics lies in Pask (1969). According to Frazer’s (2001) retrospective, Pask claimed that architecture and cybernetics share a common philosophy in the same sense that Beer had shown for operations research, and argued that architects are first and foremost system designers (Frazer’s full text; Pask’s original has not been read). Glanville (2007) places cybernetics as the theory of design and design as cybernetics in practice, in a complementary relation (abstract). Pickering (2002) connects the materiality of the devices and systems of Ashby, Beer, and Pask to STS, and places the lineage of his concept of the mangle (Pickering 1995) in cybernetics (abstract; for the 1995 book, bibliographic record only).

In the 2010s, the same distinction was restated on the HCI side. Farooq and Grudin (2016) distinguish interaction, as stimulus and response, from integration, as a partnership between humans and computers. They describe integration as a continuum that extends interaction rather than replacing it, and write that an era of “integration as a partnership or symbiotic relationship in a broad sense” is coming (abstract). Akata et al. (2020) define hybrid intelligence as combining human and machine intelligence to augment, rather than replace, human capabilities and to reach goals that neither could reach alone (full text). Raisch and Krakowski (2021) argue, from management studies, that automation and augmentation are interdependent and produce a paradox (abstract). Berretta et al. (2023) showed that a wide range of terms is used inconsistently in the literature on human-AI teaming (full text). Sarkar (2023) argues that calling interaction with AI “collaboration” is misleading, that it takes credit for the AI’s “labor” away from the people behind it and hides exploitative arrangements, and proposes that viewing AI as a tool or instrument is more accurate and fairer (abstract). How to distinguish the uses of symbiosis, augmentation, integration, collaboration, and teaming is therefore the first problem that the review has to settle.

The critiques of the 1980s and the entry into design research

Winograd and Flores (1986) criticized the vision of symbiosis from the side of AI, alongside Suchman’s situated action and Dreyfus’s critique. These works are in the corpus of the reading list, and their content was confirmed within the scope of Maurer’s (1991) account (Essential Readings for Researching the Philosophy of Design in the Age of AI (A Guide by Current)). What we added here are the entry points through which design research, after these critiques, began to treat AI again as a “material.” Dove et al. (2017) stated, based on a survey of practitioners, that machine learning is a new and difficult design material in UX practice and has not gone through the design innovation that other technologies have (abstract). The two papers by Yang et al. (2018) classified 2,494 HCI papers on machine learning to show starting points for design innovation, and, through interviews with 13 experienced designers, described a practice in which designers achieve results through ongoing collaboration with data scientists without regarding themselves as machine learning experts (abstracts). Yang et al. (2020) and Benjamin et al. (2021) continue this line and are in the corpus of the reading list.

Co-creation frameworks in HCI (1999 to 2024)

The lineage of co-creation frameworks can be traced from Horvitz (1999). Horvitz presented principles for mixed-initiative user interfaces, which combine automated reasoning services with direct manipulation, using the scheduling system LookOut (full text). Davis (2013) identified a gap in the literature on creativity support tools concerning how a computational colleague contributes to the creative process, and proposed a system for drawing together (full text). Yannakakis et al. (2014) defined mixed-initiative co-creativity and explained diagrammatic lateral thinking on the basis of the theory of the extended mind (Clark and Chalmers 1998) (full text). Kantosalo and Toivonen (2016) distinguished alternating co-creativity, in which human and computational agents build in turns, from task-divided co-creativity, in which they share out the tasks, and formalized the former for the one-to-one case (full text). Deterding et al. (2017) took as the starting point of mixed-initiative creative interfaces the way in which, in creative dialogue between people, both sides alternately constrain, suggest, produce, evaluate, modify, and select (full text). Guzdial and Riedl (2019) proposed a general framework for turn-based interaction (full text). Rezwana and Maher (2023) coded 92 existing co-creative systems with COFI, a framework for describing interaction, and derived three interaction models (full text).

During the same period, a framework with control and automation as two axes, and design guidelines, appeared. Shneiderman (2020) proposed a two-dimensional framework that separates the level of automation from the level of human control, and called for high control and high automation at the same time (full text). Amershi et al. (2019) presented 18 guidelines for human-AI interaction, validated with practitioners (full text), and Weisz et al. (2024) proposed six principles for generative AI applications, three of which reinterpret known problems for generative AI and three of which address characteristics specific to generative AI (full text). These works form a lineage that translated the vision of symbiosis into types of interaction concerned with “who takes the initiative,” and of Licklider’s two aims (facilitating formulation and sharing decisions) they mainly address the former.

Design-specific evidence (2017 to 2026)

The existing corpora (AI in the Design Industry: An Academic Review (2026), Does Prototyping Widen Design Exploration? Fixation on Early Examples, Parallel Prototyping, Rapid Prototyping with Generative AI, and Documentation in Research through Design) contain empirical studies such as Gmeiner et al. (2023), Tholander and Jonsson (2023), and Wadinambiarachchi et al. (2024). What we added here are studies that directly observed “which side takes the lead” in symbiosis, and consequences at the level of groups.

Figoli et al. (2022) used three AI tools at the research, sketching, and coloring stages in a workshop with 16 participants and treated human-AI collaboration as teamwork (full text). Cautela et al. (2019), based on cases of AI startups, identified the strengthening of the research stage and the automation of the prototyping and learning stages as changes (full text). Lawton et al. (2023) examined Reframer, in which a person and the system draw at the same time, with 12 and 32 participants, and proposed a model of the conditions under which users experience co-creative AI as a collaborator rather than as a tool without agency (abstract). Chiou et al. (2023) reported, from research through design with 6 designers, that image generation brings in different perspectives (abstract). Zhang et al. (2021) stated, in a human-subject experiment in which the problem changed partway through, that AI helped low-performing teams and hurt high-performing teams before the problem changed (abstract).

On consequences at the group level, Doshi and Hauser (2024; 293 writers; individual creativity rose and the works became more similar to each other), Anderson et al. (2024; 36 participants; ideas generated with ChatGPT were semantically similar), and Shaer et al. (2024; adding an LLM to group brainwriting) were confirmed in their full texts. Inie et al. (2023) collected the worries and expectations of 23 creative professionals (full text), and Epstein et al. (2023) stated that generative AI unsettles conventional notions of authorship, ownership, inspiration, and sampling (full text). Hernández-Ramírez and Ferreira (2024) argue that anxiety about the end of design work is rooted in a misunderstanding of design produced by managerialism, which prioritizes efficiency and countable outcomes, and they reframe design as craft-based knowledge work (abstract).

The philosophical foundations of symbiosis and their critiques

The philosophy that could support the vision of symbiosis lies along three lines. The first is the line in which cognition extends outside the head: the active externalism of Clark and Chalmers (1998) (full text), the distributed cognition of Hutchins (1995) (publisher’s description), its application to HCI by Hollan et al. (2000) (abstract), and the material engagement of Malafouris (2013) (publisher’s description). That Yannakakis et al. (2014) cite the extended mind was confirmed in their full text. The second is the line of mediation: Verbeek (2015) states that mediation theory enables designers to anticipate the impact of products on human practices and experiences, and contrasts it with a dialectical view that sees humans and technology as opposed (abstract). For Verbeek’s (2008) cyborg intentionality, only the bibliographic record was confirmed, and Ihde and Verbeek (2006) are in the corpus of the reading list. The third is the line of practice: the mangle of Pickering (1995) (bibliographic record only) and Pickering (2002) (abstract) connect to the lineage of cybernetics.

We collected critiques in two lineages. For the human factors of automation, we include Bainbridge’s (1983) ironies (the full text was checked in the corpus of What Should Creators Be Able to Take Over When Generative AI Does the Making? (Lessons from Aviation Automation Research)), the four functions and levels of automation of Parasuraman et al. (2000) (abstract), and Elish’s (2019) moral crumple zone (operators with limited control are blamed for failures of automated systems; full text). For critiques of terminology, there are Sarkar (2023) and Berretta et al. (2023), mentioned above, and Coeckelbergh (2017) sets out a framework for asking whether machines can create art (abstract). The meta-analysis by Vaccaro et al. (2024), based on 106 experiments and 370 effect sizes, reported that human-AI combinations performed on average worse than the best of humans or AI alone (Hedges’ g = −0.23, 95 percent confidence interval −0.39 to −0.07). The same meta-analysis reported that the losses were concentrated in decision tasks, and that the gains were larger in content creation tasks (full text). Because design tasks include both decision and creation, the review can ask in its section on empirical evidence whether this division also holds in design.

More-than-human design

For the lineage that removes the premise of human-centeredness, we added the following works after Forlano (2017), Giaccardi and Redström (2020), and Escobar (2018) in the corpus of the reading list. To deal with the interdependence of humans and nonhumans in IoT, Coulton and Lindley (2019) extended human-centered design beyond an anthropocentric perspective and introduced the metaphor of constellations (full text). The co-performance of Kuijer and Giaccardi (2018) looks not at the autonomy (smartness) of artifacts but at how humans and artifacts learn and perform side by side, and moves the focus of design from the allocation of agency at design time to embodied learning in everyday practice (abstract).

Frauenberger (2019) called the entanglement of humans and digital technology the next wave of HCI (abstract). Nicenboim et al. (2023) traced decentering from HCI to critical posthumanism and divided it into five dimensions (abstract), and Nicenboim, Giaccardi, and Redström (2023) reported experiments that expose the human-centered bias of conversational agents (full text). Nicenboim et al. (2024) set out three themes of designing-with in their introduction to a DRS track, and Patil et al. (2024) bring the vocabulary of hauntology into design with AI (abstract). Cila (2022) derives qualities of human-agent collaboration from Bratman’s shared cooperative activity (abstract). For Wakkary (2021) and Redström and Wiltse (2019), only publisher’s descriptions were available, and for Haraway (1985) and Barad (2007), only bibliographic records; the full texts of these originals were not retrieved.

A map of existing reviews (the Related Work axis)

To the 9 reviews in the existing corpus (Hughes et al. 2021, Fang et al. 2025, Choudhury et al. 2025, Ahmed and Imran 2025, Holzner et al. 2025, Heigl 2025, Christodoulou 2026, Luo 2025, Tsang and Lee 2022), we added the following 11.

No review taking the approach of “placing the literature again on the currents of the philosophy of design” was found in OpenAlex searches (the top 12 to 25 results of each of "philosophy of design" "artificial intelligence", 972 results; "design theory" "generative AI" review, 1,693 results; and "design philosophy" "generative AI", 1,895 results). The closest is Figoli et al. (2025), which is close in mapping researchers’ positions. Because its full text could not be retrieved, whether it uses the lineage of philosophy as an axis has not been confirmed.

We examined whether existing reviews cite the classics, using the reference lists in OpenAlex (referenced_works) and the reference strings in Crossref. The targets were 13 reviews, the 11 above plus Rezwana and Maher (2023), Mirzaei et al. (2026), and Hughes et al. (2021), and the classics checked were Licklider 1960, Negroponte 1970, Suchman 2007, Schön 1983, Simon 1969, Verbeek 2006, Ihde 1990, Cross 2001, Dove 2017, Yang 2020, Clark and Chalmers 1998, Hutchins 1995, Rittel and Webber 1973, Engelbart 1962, Pask 1969, Winograd and Flores 1986, Latour 1992, Akrich 1992, and Dreyfus. The results were as follows.

• Licklider 1960 is cited by 2 reviews: Chen et al. 2025 and Vaccaro et al. 2024.
• Simon 1969 is cited by 1 review, Figoli et al. 2025. Suchman 1987 is cited by 1 review, Zhang et al. 2025. Rittel and Webber are cited by 1 review, Mirzaei et al. 2026. Cross 2001 is cited by 2 reviews, Figoli et al. 2025 and Ramachandran 2026.
• Dove 2017 or Yang 2020 is cited by 3 reviews: Shi et al. 2023, Rezwana and Maher 2023, and Hughes et al. 2021.
• Negroponte, Engelbart, Pask, Winograd and Flores, Schön, Verbeek, Ihde, Latour, Akrich, Hutchins, Clark and Chalmers, and Dreyfus are cited by 0 reviews through either route.

However, the reference lists in OpenAlex are incomplete (Figoli and Rampino 2026 has 0 entries, and there is no record of the book by Winograd and Flores), and Crossref references are attached to only 8 of the reviews. Recounting by reading the reference sections of the 13 reviews directly was left to the recipient of the handoff.

Axes for rearrangement (provisional)

If the six lineages of this note are placed on the questions raised by the reading list (who makes the judgment, what the one who designs knows, how artifacts mediate action, whose world design makes, and whether human-centeredness can be maintained), the correspondence is as follows.

Applied to the form of the rationale sentence for the RW axis (separating x and y, which were on the same shelf in review A, and connecting x and z, which were on different shelves in review B, makes judgment J possible), the two provisional candidates are as follows.

• Separate “tools for ideation support” (COFI, Lawton, Chiou) and “machine learning as a material” (Dove, Yang, Benjamin), which are on the same shelf (AI helping designers, allocation of agency) in Shi et al. 2023 and Zhang et al. 2025, by the questions “who makes the judgment” and “what can be known about the material.”
• Connect “symbiosis,” “augmentation,” “integration,” and “collaboration,” which are on different shelves in Berretta et al. 2023 (variation in terminology), through the definitions of Licklider, Engelbart, Farooq and Grudin, and Sarkar, so that one can judge which promise of which term (whether humans set the goals, whether machines help with formulation, or whether both act autonomously) each study from the 2020s stands on.

Both candidates needed to pass, at the recipient of the handoff, a removal test (whether the judgment fails when the lineage is removed) and a substitution test (if the same argument holds when an unrelated lineage is substituted, the novelty is an illusion).

Judgment by paper-pipeline: This preparation was handed off to paper-pipeline, but all of about 120 proposals over five rounds were judged FAIL, and the work was frozen. The deciding evidence was that recent research from 2023 to 2026, and the very sources cited in the handoff, had already stated the core of the reworked proposals. In one case, a different page of the same paper, outside the pages cited in the gap column of the handoff, already held the answer (Sarkar 2023, p.7, according to the check by paper-pipeline).

Missing sources and full texts not reached

• Among the original sources on symbiosis, the full texts of Negroponte 1970 and 1976, Steenson 2017, and Pask 1969 were not retrieved. Without reading Negroponte’s chapter “Architect-Machine Symbiosis,” the vision of symbiosis on the design side cannot be described.
• The critiques of the 1980s (Winograd and Flores, Suchman, Dreyfus) are covered only within the corpus of the reading list, without the full texts of the originals.
• Among the existing reviews, the full text of Figoli et al. 2025 (Design Studies) was not reached, so the nearest candidate on the RW axis has not been read. Shi et al. 2023, Stige et al. 2024, and Chen et al. 2025 are also covered only within their abstracts.
• No secondary source that contrasts the three terms symbiosis, augmentation, and cybernetics was found in this search.
• Searches by journal found few AI papers in Design Issues, CoDesign, and the International Journal of Design, and for the AI tracks of DRS 2024 and 2026 only the numbers of papers were checked, not their content.
• Japanese-language sources (Yoshikawa’s general design theory and AI, and papers on AI in Japanese design research) were not included in the scope of this note.

Unverified items

• Figoli et al. 2025 (Design Studies): full text not reached (ScienceDirect returned a JavaScript challenge page, and the Polimi institutional repository returned 403). Only the scope of the abstract, a map of researchers’ positions, is described.
• The chapter “Architect-Machine Symbiosis” in Negroponte 1970: confirmed only through the table of contents; the content has not been read.
• Pask 1969: the volume, month, and pages of the original are as given in secondary sources and have no record in Crossref or OpenAlex. The DOI of the reprint (De Gruyter 2023) is given in the references.
• The content of Steenson 2017: within the scope of the bookseller’s catalog and the Crossref table of contents.
• The full-text retrieval record consists of 36 full texts, 38 abstracts or publisher descriptions, and 7 not retrieved, out of 81 URLs (source/review/ai-design-symbiosis-review/fulltext-manifest.json). For sources described from abstracts only and sources with bibliographic records only, the scope of confirmation is given in the corpus table. Sources with abstracts only are described only within what their abstracts state.
• The count of whether existing reviews cite the classics depends on the reference lists in OpenAlex and Crossref. It has not been recounted from the reference sections of the texts.

References

The lineage of symbiosis (originals and histories)

• Licklider, J. C. R. 1960. “Man-Computer Symbiosis.” IRE Transactions on Human Factors in Electronics HFE-1 (1): 4–11. https://doi.org/10.1109/THFE2.1960.4503259
• Engelbart, Douglas C. 1962. Augmenting Human Intellect: A Conceptual Framework. SRI Summary Report AFOSR-3223. Menlo Park, CA: Stanford Research Institute. https://www.dougengelbart.org/content/view/138
• Wiener, Norbert. 1948. Cybernetics: Or Control and Communication in the Animal and the Machine. Cambridge, MA: MIT Press (the DOI is for the 2019 reissue). https://doi.org/10.7551/mitpress/11810.001.0001
• Negroponte, Nicholas. 1970. The Architecture Machine: Toward a More Human Environment. Cambridge, MA: MIT Press. https://doi.org/10.7551/mitpress/8269.001.0001
• Negroponte, Nicholas. 1976. Soft Architecture Machines. Cambridge, MA: MIT Press. https://doi.org/10.7551/mitpress/6317.001.0001
• Pask, Gordon. 1969. “The Architectural Relevance of Cybernetics.” Architectural Design 39 (September): 494–496 Reprinted in Interactive Design, 27–34. Berlin: De Gruyter, 2023. https://doi.org/10.1515/9783035626896-003
• Frazer, John H. 2001. “The Cybernetics of Architecture: A Tribute to the Contribution of Gordon Pask.” Kybernetes 30 (5/6): 641–651. https://doi.org/10.1108/03684920110391896
• Glanville, Ranulph. 2007. “Try Again. Fail Again. Fail Better: The Cybernetics in Design and the Design in Cybernetics.” Kybernetes 36 (9/10): 1173–1206. https://doi.org/10.1108/03684920710827238
• Steenson, Molly Wright. 2017. Architectural Intelligence: How Designers and Architects Created the Digital Landscape. Cambridge, MA: MIT Press. https://doi.org/10.7551/mitpress/10971.001.0001
• Pickering, Andrew. 2002. “Cybernetics and the Mangle: Ashby, Beer and Pask.” Social Studies of Science 32 (3): 413–437. https://doi.org/10.1177/0306312702032003003
• Farooq, Umer, and Jonathan Grudin. 2016. “Human-Computer Integration.” Interactions 23 (6): 26–32. https://doi.org/10.1145/3001896

Co-creation frameworks in HCI

• Horvitz, Eric. 1999. “Principles of Mixed-Initiative User Interfaces.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ‘99): 159–166. https://doi.org/10.1145/302979.303030
• Lubart, Todd. 2005. “How Can Computers Be Partners in the Creative Process: Classification and Commentary on the Special Issue.” International Journal of Human-Computer Studies 63 (4–5): 365–369. https://doi.org/10.1016/j.ijhcs.2005.04.002
• Davis, Nicholas. 2013. “Human-Computer Co-Creativity: Blending Human and Computational Creativity.” Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 9 (6): 9–12. https://doi.org/10.1609/aiide.v9i6.12603
• Yannakakis, Georgios N., Antonios Liapis, and Constantine Alexopoulos. 2014. “Mixed-Initiative Co-Creativity.” In Proceedings of the 9th International Conference on the Foundations of Digital Games (FDG 2014). https://www.antoniosliapis.com/papers/mixed_initiative_co-creativity.pdf
• Kantosalo, Anna, and Hannu Toivonen. 2016. “Modes for Creative Human-Computer Collaboration: Alternating and Task-Divided Co-Creativity.” In Proceedings of the Seventh International Conference on Computational Creativity (ICCC 2016): 77–84. https://www.cs.helsinki.fi/u/htoivone/pubs/iccc_2016_kantosalo_toivonen.pdf
• Deterding, Sebastian, Jonathan Hook, Rebecca Fiebrink, Marco Gillies, Jeremy Gow, Memo Akten, Gillian Smith, Antonios Liapis, and Kate Compton. 2017. “Mixed-Initiative Creative Interfaces.” In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA ‘17): 628–635. https://doi.org/10.1145/3027063.3027072
• Guzdial, Matthew, and Mark Riedl. 2019. “An Interaction Framework for Studying Co-Creative AI.” arXiv:1903.09709. https://arxiv.org/abs/1903.09709
• Rezwana, Jeba, and Mary Lou Maher. 2023. “Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems.” ACM Transactions on Computer-Human Interaction 30 (5): 1–28. https://doi.org/10.1145/3519026
• Shneiderman, Ben. 2020. “Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy.” International Journal of Human–Computer Interaction 36 (6): 495–504. https://doi.org/10.1080/10447318.2020.1741118
• Amershi, Saleema, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, et al. 2019. “Guidelines for Human-AI Interaction.” In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ‘19): 1–13. https://doi.org/10.1145/3290605.3300233
• Weisz, Justin D., Jessica He, Michael Muller, Gabriela Hoefer, Rachel Miles, and Werner Geyer. 2024. “Design Principles for Generative AI Applications.” In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI ‘24): 1–22. https://doi.org/10.1145/3613904.3642466

Design-specific evidence and concepts

• Dove, Graham, Kim Halskov, Jodi Forlizzi, and John Zimmerman. 2017. “UX Design Innovation: Challenges for Working with Machine Learning as a Design Material.” In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI ‘17): 278–288. https://doi.org/10.1145/3025453.3025739
• Yang, Qian, Nikola Banovic, and John Zimmerman. 2018. “Mapping Machine Learning Advances from HCI Research to Reveal Starting Places for Design Innovation.” In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI ‘18): 1–11. https://doi.org/10.1145/3173574.3173704
• Yang, Qian, Alex Scuito, John Zimmerman, Jodi Forlizzi, and Aaron Steinfeld. 2018. “Investigating How Experienced UX Designers Effectively Work with Machine Learning.” In Proceedings of the 2018 Designing Interactive Systems Conference (DIS ‘18): 585–596. https://doi.org/10.1145/3196709.3196730
• Figoli, Fabio Antonio, Francesca Mattioli, and Lucia Rampino. 2022. Artificial Intelligence in the Design Process: The Impact on Creativity and Team Collaboration. Milan: FrancoAngeli. https://series.francoangeli.it/index.php/oa/catalog/book/781 (PDF https://library.oapen.org/bitstream/20.500.12657/53627/1/9788835134640.pdf)
• Cautela, Cabirio, Marzia Mortati, Claudio Dell’Era, and Luca Gastaldi. 2019. “The Impact of Artificial Intelligence on Design Thinking Practice: Insights from the Ecosystem of Startups.” Strategic Design Research Journal 12 (1): 114–134. https://doi.org/10.4013/sdrj.2019.121.08
• Zhang, Guanglu, Ayush Raina, Jonathan Cagan, and Christopher McComb. 2021. “A Cautionary Tale about the Impact of AI on Human Design Teams.” Design Studies 72: 100990. https://doi.org/10.1016/j.destud.2021.100990
• Lawton, Tomas, Kazjon Grace, and Francisco J. Ibarrola. 2023. “When Is a Tool a Tool? User Perceptions of System Agency in Human–AI Co-Creative Drawing.” In Proceedings of the 2023 ACM Designing Interactive Systems Conference (DIS ‘23): 1978–1996. https://doi.org/10.1145/3563657.3595977
• Chiou, Li-Yuan, 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 (DIS ‘23): 1941–1954. https://doi.org/10.1145/3563657.3596001
• Inie, Nanna, Jeanette Falk, and Steve Tanimoto. 2023. “Designing Participatory AI: Creative Professionals’ Worries and Expectations about Generative AI.” In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA ‘23): 1–8. https://doi.org/10.1145/3544549.3585657
• Epstein, Ziv, Aaron Hertzmann, the Investigators of Human Creativity, Memo Akten, Hany Farid, Jessica Fjeld, Morgan R. Frank, et al. 2023. “Art and the Science of Generative AI.” Science 380 (6650): 1110–1111. https://doi.org/10.1126/science.adh4451
• Doshi, Anil R., and Oliver P. Hauser. 2024. “Generative AI Enhances Individual Creativity but Reduces the Collective Diversity of Novel Content.” Science Advances 10 (28): eadn5290. https://doi.org/10.1126/sciadv.adn5290
• Anderson, Barrett R., Jash Hemant Shah, and Max Kreminski. 2024. “Homogenization Effects of Large Language Models on Human Creative Ideation.” In Proceedings of the 16th Conference on Creativity & Cognition (C&C ‘24): 413–425. https://doi.org/10.1145/3635636.3656204
• Shaer, Orit, Angelora Cooper, Osnat Mokryn, Andrew L. Kun, and Hagit Ben Shoshan. 2024. “AI-Augmented Brainwriting: Investigating the Use of LLMs in Group Ideation.” In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI ‘24): 1–17. https://doi.org/10.1145/3613904.3642414
• Hernández-Ramírez, Rodrigo, and Jorge Brandão Ferreira. 2024. “The Future End of Design Work: A Critical Overview of Managerialism, Generative AI, and the Nature of Knowledge Work, and Why Craft Remains Relevant.” She Ji: The Journal of Design, Economics, and Innovation 10 (4): 414–440. https://doi.org/10.1016/j.sheji.2024.11.002

Philosophical foundations of symbiosis and critiques

• Clark, Andy, and David Chalmers. 1998. “The Extended Mind.” Analysis 58 (1): 7–19. https://doi.org/10.1093/analys/58.1.7
• Hutchins, Edwin. 1995. Cognition in the Wild. Cambridge, MA: MIT Press. https://doi.org/10.7551/mitpress/1881.001.0001
• Hollan, James, Edwin Hutchins, and David Kirsh. 2000. “Distributed Cognition: Toward a New Foundation for Human-Computer Interaction Research.” ACM Transactions on Computer-Human Interaction 7 (2): 174–196. https://doi.org/10.1145/353485.353487
• Malafouris, Lambros. 2013. How Things Shape the Mind: A Theory of Material Engagement. Cambridge, MA: MIT Press. https://doi.org/10.7551/mitpress/9476.001.0001
• Pickering, Andrew. 1995. The Mangle of Practice: Time, Agency, and Science. Chicago: University of Chicago Press. https://doi.org/10.7208/chicago/9780226668253.001.0001
• Verbeek, Peter-Paul. 2008. “Cyborg Intentionality: Rethinking the Phenomenology of Human–Technology Relations.” Phenomenology and the Cognitive Sciences 7 (3): 387–395. https://doi.org/10.1007/s11097-008-9099-x
• Verbeek, Peter-Paul. 2015. “Beyond Interaction: A Short Introduction to Mediation Theory.” Interactions 22 (3): 26–31. https://doi.org/10.1145/2751314
• Coeckelbergh, Mark. 2017. “Can Machines Create Art?” Philosophy & Technology 30 (3): 285–303. https://doi.org/10.1007/s13347-016-0231-5
• Bainbridge, Lisanne. 1983. “Ironies of Automation.” Automatica 19 (6): 775–779. https://doi.org/10.1016/0005-1098%2883%2990046-8
• Parasuraman, Raja, Thomas B. Sheridan, and Christopher D. Wickens. 2000. “A Model for Types and Levels of Human Interaction with Automation.” IEEE Transactions on Systems, Man, and Cybernetics—Part A 30 (3): 286–297. https://doi.org/10.1109/3468.844354
• Elish, Madeleine Clare. 2019. “Moral Crumple Zones: Cautionary Tales in Human-Robot Interaction.” Engaging Science, Technology, and Society 5: 40–60. https://doi.org/10.17351/ests2019.260
• Raisch, Sebastian, and Sebastian Krakowski. 2021. “Artificial Intelligence and Management: The Automation–Augmentation Paradox.” Academy of Management Review 46 (1): 192–210. https://doi.org/10.5465/amr.2018.0072
• Akata, Zeynep, Dan Balliet, Maarten de Rijke, Frank Dignum, Virginia Dignum, Guszti Eiben, Antske Fokkens, et al. 2020. “A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect with Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence.” Computer 53 (8): 18–28. https://doi.org/10.1109/MC.2020.2996587
• National Academies of Sciences, Engineering, and Medicine. 2022. Human-AI Teaming: State-of-the-Art and Research Needs. Washington, DC: The National Academies Press. https://doi.org/10.17226/26355
• Sarkar, Advait. 2023. “Enough With ‘Human-AI Collaboration’.” In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA ‘23): 1–8. https://doi.org/10.1145/3544549.3582735
• Billingsley, William. 2025. “The Practical Epistemologies of Design and Artificial Intelligence.” Science & Education 34 (2): 807–824. https://doi.org/10.1007/s11191-024-00517-z
• Dalsgaard, Peter. 2025. “Scaffolding Metacognition in Design with Generative AI.” In Proceedings of IASDR 2025: Design Next. https://doi.org/10.21606/iasdr.2025.426
• Bradbury, Jacob, Pierre Alexandre Tremblay, Owen Green, and Ted Moore. 2026. “Resisting Reconfiguration: Contrasting Design Philosophies in Creative AI.” Technoetic Arts 24 (1): 131–147. https://doi.org/10.1386/tear_00152_1

More-than-human and posthuman design

• Haraway, Donna. 1985. “A Manifesto for Cyborgs: Science, Technology, and Socialist Feminism in the 1980s.” Socialist Review 15 (80): 65–107 Reprinted in Simians, Cyborgs, and Women (1991). https://doi.org/10.4324/9780203873106
• Barad, Karen. 2007. Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Durham, NC: Duke University Press. https://doi.org/10.1215/9780822388128
• Kuijer, Lenneke, and Elisa Giaccardi. 2018. “Co-Performance: Conceptualizing the Role of Artificial Agency in the Design of Everyday Life.” In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI ‘18): 1–13. https://doi.org/10.1145/3173574.3173699
• Redström, Johan, and Heather Wiltse. 2019. Changing Things: The Future of Objects in a Digital World. London: Bloomsbury. https://doi.org/10.5040/9781350004368
• Coulton, Paul, and Joseph Galen Lindley. 2019. “More-Than Human Centred Design: Considering Other Things.” The Design Journal 22 (4): 463–481. https://doi.org/10.1080/14606925.2019.1614320
• Frauenberger, Christopher. 2019. “Entanglement HCI: The Next Wave?” ACM Transactions on Computer-Human Interaction 27 (1): 1–27. https://doi.org/10.1145/3364998
• Wakkary, Ron. 2021. Things We Could Design: For More Than Human-Centered Worlds. Cambridge, MA: MIT Press. https://doi.org/10.7551/mitpress/13649.001.0001
• Cila, Nazli. 2022. “Designing Human-Agent Collaborations: Commitment, Responsiveness, and Support.” In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ‘22): 1–18. https://doi.org/10.1145/3491102.3517500
• Nicenboim, Iohanna, Doenja Oogjes, Heidi Biggs, and Seowoo Nam. 2023. “Decentering Through Design: Bridging Posthuman Theory with More-than-Human Design Practices.” Human–Computer Interaction 40 (1–4): 195–220. https://doi.org/10.1080/07370024.2023.2283535
• Nicenboim, Iohanna, Elisa Giaccardi, and Johan Redström. 2023. “Designing More-Than-Human AI: Experiments on Situated Conversations and Silences.” diid — disegno industriale industrial design 80. https://doi.org/10.30682/diid8023c
• Nicenboim, Iohanna, Joseph Lindley, Cristina Zaga, Arne Berger, Laura Forlano, and Elisa Giaccardi. 2024. “More-Than-Human Design in Practice.” In Proceedings of DRS 2024: Boston. https://doi.org/10.21606/drs.2024.114
• Patil, Mugdha, Nazli Cila, Johan Redström, and Elisa Giaccardi. 2024. “In Conversation with Ghosts: Towards a Hauntological Approach to Decolonial Design for/with AI Practices.” CoDesign 20 (1): 55–76. https://doi.org/10.1080/15710882.2024.2320269

Existing reviews

• Shi, Yang, Tian Gao, Xiaohan Jiao, and Nan Cao. 2023. “Understanding Design Collaboration Between Designers and Artificial Intelligence: A Systematic Literature Review.” Proceedings of the ACM on Human-Computer Interaction 7 (CSCW2): 1–35. https://doi.org/10.1145/3610217
• Stige, Åsne, Efpraxia D. Zamani, Patrick Mikalef, and Yuzhen Zhu. 2024. “Artificial Intelligence (AI) for User Experience (UX) Design: A Systematic Literature Review and Future Research Agenda.” Information Technology & People 37 (6): 2324–2352. https://doi.org/10.1108/itp-07-2022-0519
• Berretta, Sophie, Alina Tausch, Greta Ontrup, Björn Gilles, Corinna Peifer, and Annette Kluge. 2023. “Defining Human-AI Teaming the Human-Centered Way: A Scoping Review and Network Analysis.” Frontiers in Artificial Intelligence 6: 1250725. https://doi.org/10.3389/frai.2023.1250725
• Vaccaro, Michelle, Abdullah Almaatouq, and Thomas Malone. 2024. “When Combinations of Humans and AI Are Useful: A Systematic Review and Meta-Analysis.” Nature Human Behaviour 8 (12): 2293–2303. https://doi.org/10.1038/s41562-024-02024-1
• Figoli, Fabio Antonio, Rik Bruggeman, Lucia Rampino, and Paolo Ciuccarelli. 2025. “AI-against-Design Map: A Systematic Review of 20 Years of AI-Focused Studies in Design Research.” Design Studies 96: 101279. https://doi.org/10.1016/j.destud.2024.101279
• Zhang, Shuning, Han Wang, and Xin Yi. 2025. “Exploring Collaboration Patterns and Strategies in Human-AI Co-Creation through the Lens of Agency: A Scoping Review of the Top-Tier HCI Literature.” Proceedings of the ACM on Human-Computer Interaction 9 (7): 1–43. https://doi.org/10.1145/3757594 (arXiv version https://arxiv.org/abs/2507.06000)
• Chen, Guoyu, Zhiwei Yu, Yiwen Xie, Zhen Liu, and Chuan Yu. 2025. “The Study of Human-AI Co-Creation Design under Generative Artificial Intelligence: Cognition, Process, Method, and Outcome.” Journal of Engineering Design 37 (4): 1710–1751. https://doi.org/10.1080/09544828.2025.2567155
• Kadenhe, Nyasha, Mohammed Al Musleh, and Andrew Lompot. 2025. “Human-AI Co-Design and Co-Creation: A Review of Emerging Approaches, Challenges, and Future Directions.” Proceedings of the AAAI Symposium Series 6 (1): 265–270. https://doi.org/10.1609/aaaiss.v6i1.36061
• Figoli, Fabio Antonio, and Lucia Rampino. 2026. “Mapping the Human-AI Collaboration Trends in Design Research: A Systematic Review.” Interaction Design and Architecture(s) 69: 258–285. https://doi.org/10.55612/s-5002-069-012 (PDF https://ixdea.org/wp-content/uploads/IxDEA_art/69/69_12.pdf)
• Singh, Saloni, Koen V. Hindriks, Dirk Heylen, and Kim Baraka. 2026. “A Systematic Review of Artistic Human-AI Co-Creative Systems and Interaction Design.” ACM Transactions on Interactive Intelligent Systems. https://doi.org/10.1145/3834857 (arXiv version https://arxiv.org/abs/2506.21333)
• Ramachandran, R. 2026. “AI-Augmented Design Creativity: A Review of Human-AI Collaboration and the Conditions for Creative Enhancement.” Design and Artificial Intelligence 2 (3): 100104. https://doi.org/10.1016/j.daai.2026.100104

Related notes

• Essential Readings for Researching the Philosophy of Design in the Age of AI (A Guide by Current): a reading list by current (a corpus of 49 sources)
• AI in the Design Industry: An Academic Review (2026): an academic review of the use of AI in the design industry (including the existing reviews P03 to P10)
• Does Prototyping Widen Design Exploration? Fixation on Early Examples, Parallel Prototyping, Rapid Prototyping with Generative AI, and Documentation in Research through Design: prototyping and fixation with generative AI
• What Should Creators Be Able to Take Over When Generative AI Does the Making? (Lessons from Aviation Automation Research): the ironies of automation and the maintenance of skills