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Event Details
Submission deadline: 15 September 2025
We are calling for empirical studies applying quantitative or qualitative methods to examine learning to design with AI in organisations.
Special issue information:
Artificial intelligence (AI) tools require designers to acquire new skills and knowledge, and the field has recognised new demands through a strong conceptual understanding of the role of AI in design. However, the relative lack of empirical studies has led to more myth and uncertainty than to clear guidelines around how to best work with AI in design. The aim of this special issue, Design and AI: Learning, Adapting, Collaborating, is to empirically advance our understanding on how designers are able to successfully leverage AI as a tool, and perhaps as a partner. Empirical contributions to the issue may include high-quality studies investigating design topics at individual, collective, or organizational levels, and using quantitative or qualitative data and methods. Submissions are expected to address basic research beyond specific commercial applications, in categories of (1) Learning to collaborate in AI-Human teams, (2) Adapting to AI-Design Methods, (3) Acquiring AI-Design Skills and (4) AI-Design Training & Education.
Guest Editors
- Prof. Clark, American University, Kogod School of Business, Washington D.C., U.S.A.
- Prof. de Bont, National University of Singapore, College of Design and Engineering, Singapore
- D.Sc. Graff, Tongji University, College of Design & Innovation, Shanghai, China
Consulting Editor
- Prof. Fan, Tongji University, College of Design & Innovation, Shanghai, China
Background
AI refers to systems that display intelligent behaviour by analysing their environment and taking actions with some degree of autonomy to achieve specific goals. (European Commission, 2018)
The rapid recent advancements of AI, including natural and large language models (LLM) and related tools, enable designers to involve AI throughout the design process, as well as the potential impact on design process and outcomes. Many AI tools have been customized in the form of software applications that address specific tasks within the design field, potentially altering the way designers work. Design work therefore becomes a product of human and non-human efforts, at some level of collaboration. This perspective is recognised by adapting Jones (1970) definition of design as the activities of a human or machine in creating or contributing to the creation of physical or conceptual things. This characterisation builds on the idea that designers (i.e., who do the design work) can be machines or other forms of intelligence, in addition to the humans who traditional have done this work. It focuses also on the design outcome: physical or conceptual things rather than on the process (i.e., creative problem-solving).
Design is a broad field with many sub-branches. To provide some focus for this special issue, we identified four main areas of research interest on Design and AI: Learning, Adapting, Collaborating: (1) Learning to collaborate in AI-Human teams, (2) Adapting to AI-Design Methods, (3) Acquiring AI-Design Skills and (4) AI-Design Training & Education.
(1) Learning to collaborate in AI-Human Teams is concerned with AI as a team member rather than just a tool for designing. While academics theorise that combining respective strengths in AI-Human Collaboration is likely to be more impactful than solely human or machine work (Song et al., 2020), research to date has shown mixed results (e.g. Wilson & Daugherty, 2018, Zhang et al., 2021). Papers for this special issue could further advance our understanding by, for example, investigating if traditional team models, such as transactive memory system, or shared mental models, or adaptation (Rico, Gibson, Sanchez-Manzanares, & Clark, 2020) remain relevant within AI-Human Collaboration.
(2) Adapting to AI-Design Method is concerned with tools use during the design process. Many AI applications, such as ChatGPT4 or others can be used by the designers during designing (i.e. research, ideation, prototyping, or implementation). Designers have little guidance on how to use AI with existing design tools (cf. Clark & Graff, 2025). In some respects, we are in a similar situation as designers in the 1970s when there was little to no guidance for product designers (Jones, 1970). Papers concerned with AI-Design methods could, for example, introduce new design methods or identify current AI-design methods used and their impact on the design outcome. Papers describing and illustrating the effectiveness of a specific AI tool are excluded from this special issue.
(3) Acquiring AI-Design Skills recognises that the skill set of a designer must potentially adapt for AI & design. Whereas some skills might be less relevant in the future, new skills are required to be able to use AI (e.g., writing prompts). Papers of this type, for example, could explore and examine the skills that may lead to a better understanding and use of AI. What are the important skills and how do designers acquire those skills? What effect has the use of AI (e.g., sketching AI) on traditional design skills and what are the potential consequences?
(4) AI-Design Training & Education focuses on AI-design skills and capacity building. In contrast to which new design skills are required, this area is concerned with how designers can acquire these new AI-design skills. This focus may include professional designers in organisational settings as well as design students in academic context. Papers concerned with teaching AI-design skills in academic context could, for example, focus on how AI can support teachers (e.g., Meron & Tekmen Araci, 2023), or specific course content (e.g., Huang, Wensveen, & Funk, 2023). Submissions with a focus on AI-design skills training might look at how organisations can up- and re-skill their design workforce.
Aim and Scope
We are calling for empirical studies applying quantitative or qualitative methods to examine learning to design with AI in organisations. Studies that are exclusively theoretical/conceptual or opinion statements are outside the scope of this special issue. A study must align with at least one of the four core themes identified above.
Proposed Timeline (Tentative Deadlines)
May 10, 2025: open call
May 20, 2025: submission portal opened
September 15, 2025: deadline for submission
January 15, 2026: author notification
March 31, 2026: deadline for revised submission
April 20, 2026: final decision notification
June 30, 2026: publication date *
* The final publication date is tentatively set but may be extended to September 30, 2026, based on the actual production timelines.
Additional Information and Queries
Mark Clark, mark.clark@american.edu
Cees de Bont, debont@nus.edu.sg
Daniel Graff, danielgraff@tongji.edu.cn
Manuscript submission information:
Short Special Issue Name
SI: Design and AI: Learning, Adapting, Collaborating
Editorial Manager URL
https://www.editorialmanager.com/sheji/default.aspx
References
Clark, M. A. & Graff, D. (forthcoming 2025). AI & Design Collaboration: Not a Full Member (Yet). In S. Paletz & S. S. Dubrow, Research on Managing Groups and Teams: AI in Teams. Emerald Publishing.
Dorst, Kees; Dijkhuis, Judith (1995). Comparing paradigms for describing design activity. Design Studies. 16 (2): 261–274. doi:10.1016/0142-694X(94)00012-3.
European Commission, Directorate-General for Communications Networks, Content and Technology. (2018). Communication from the commission to the EUROPEAN parliament, the EUROPEAN council, the council, the European economic and social committee and the committee of the regions artificial intelligence for Europe.
Gmeiner, F. Yang, H., Yao, L., Holstein, K., & Martelaro, N. (2023). Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design Tools. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23), April 23–28, 2023, Hamburg, Germany. doi.org/10.1145/3544548.3580999
Hernández-Ramírez, Rodrigo, and João Batalheiro 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): 414–40. doi.org/10.1016/j.sheji.2024.11.002.
Huang, Y.-C. J., Wensveen, S., & Funk, M. (2023). Experiential speculation in vision-based AI design education: Designing conventional and progressive AI futures. International Journal of Design, 17(2), 1-17. doi.org/10.57698/v17i2.01
Jones, John Chris. 1970. Design Methods: Seeds of Human Futures. Wiley-Interscience.
Meron Y., & Tekmen Araci, Y. (2023). Artificial intelligence in design education: evaluating ChatGPT as a virtual colleague for post-graduate course development. Design Science, 9, e30. doi:10.1017/dsj.2023.28
Rico, R., Gibson, C., Sanchez-Manzanares, M., & Clark, M. A. (2020). Team adaptation and the changing nature of work: Lessons from practice, evidence from research, and challenges for the road ahead. Australian Journal of Management, 45(3), 507-526. doi.org/10.1177/0312896220918908
Song, B., Zurita, N. S., Zhang, G., Stump, G., Balon, C., Miller, S., et al. (2020). Toward hybrid teams: A platform to understand human-computer collaboration during the design of complex engineered systems. In Proceedings of the design society: DESIGN conference, Vol. 1, pp. 1551-1560. Cambridge University Press.
Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96, 114-123.
Zhang, G., Raina, A., Cagan, J., & McComb, C. (2021). A cautionary tale about the impact of AI on human design teams. Design Studies, 72, doi.org/10.1016/j.destud.2021.100990.
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