Guest Editors

• Derya Karadağ, Işık University, Türkiye
• Pelin Bolca, Politecnico di Torino, Italy
• Faruk Can Ünal, Yeditepe University, Türkiye
• Naylor Vilas Boas, Federal University of Rio de Janeiro (UFRJ), Brazil

Important dates

• Submission deadline:  February 21st 2027
• Notification to the authors: May 15th 2027
• Camera ready paper: June 15th 2027
• Publication of the special issue: Summer 2027

Overview

Generative artificial intelligence is rapidly becoming part of contemporary design practice and education. Text-to-image generators, large language models, and multimodal systems increasingly participate in ideation, visualization, representation, iteration, and conceptual exploration. In educational settings, these technologies can expand the range of alternatives students encounter and introduce new forms of interaction between designers and computational systems. Yet their significance extends beyond the introduction of another generation of digital tools. As generative systems become capable of performing activities traditionally embedded in learning to design, a fundamental question emerges: What should designers now know, what should they be able to do, and how should these capacities be taught?

Design education has traditionally developed knowledge and judgement through making, representation, iteration, critique, and reflection. Generative AI changes some of the conditions under which these processes occur. Students can generate numerous visual and conceptual alternatives with unprecedented speed, yet the ability to generate does not eliminate the need for disciplinary knowledge or design judgement. Instead, it raises questions about the knowledge required to interpret, evaluate, contextualize, and transform computationally generated possibilities. This reconsideration also concerns how students engage with historical, cultural, and contextual knowledge, particularly in fields such as digital heritage and urban history, where generative systems may influence how knowledge is interpreted, represented, and communicated. These developments further raise questions about inclusive approaches to design learning and the representation of diverse cultural perspectives.

At the same time, generative AI complicates established understandings of design skill. Drawing, modelling, representation, and conceptual development have traditionally served as means of producing design outcomes and as essential modes of thinking. Design education must therefore consider how existing skills are being augmented or reconfigured and which new competencies are becoming necessary, including critical evaluation, curation, conceptual framing, the interpretation of historical and cultural representations, AI literacy, and reflective judgement.

These changes extend to pedagogy. If students can rapidly generate design alternatives, teaching and critique may need to emphasize why particular alternatives are selected, rejected, transformed, or developed. This also raises questions about how students learn to critically engage with AI-generated interpretations of historical, cultural, and environmental contexts. How should design reasoning be assessed when parts of the generative process are computational? How should students demonstrate authorship and agency? And how might the role of the educator change?

This Special Issue invites theoretical, empirical, comparative, and practice-based contributions across design disciplines, examining the implications of generative AI for three interconnected dimensions: knowledge, skills, and pedagogy. It seeks to move beyond tool adoption toward reconsidering what is taught, how design capabilities are developed, and how learning is evaluated..

Topics of Interest

We invite theoretical, empirical, comparative, and practice-based contributions addressing the implications of generative AI for design knowledge, skills, and pedagogy.

Topics of interest include, but are not limited to:

• Design knowledge and expertise, e.g. rethinking disciplinary knowledge, design judgement, critical evaluation, and decision-making in AI-mediated design processes.
• Design skills and emerging competencies, e.g. AI literacy, prompting, curation, conceptual framing, and the changing roles of drawing, modelling, representation, and making.
• Design pedagogy and assessment, e.g. AI-mediated studio and project-based learning, critique and reflective practices, curriculum development, learning outcomes, and the changing roles of design educators.
• Human–AI co-creation and creative agency, e.g. authorship, originality, student autonomy, human–AI collaboration, and the ethical and social implications of generative AI in design education.
• Interdisciplinary and inclusive contexts of design education, e.g. generative AI in digital heritage and urban history education, inclusive and participatory design, interaction and experience design, computational and immersive design, and sustainability-oriented design learning.

Submission procedure 

All submissions must be original and not under review elsewhere.
Manuscripts should be submitted anonymized either in .doc or in .pdf format.
All papers will be blindly peer-reviewed by at least two reviewers. Prospective participants are invited to submit an 8-20 pages paper (including authors’ information, abstract, tables, figures, and references).
Papers should be written according to the IxD&A authors’ guidelines .
Submission page -> link
(when submitting the paper please choose the section: ‘SI: Teaching Design After Generative AI‘)

For scientific advice and for any query please contact the guest editors:

• derya.karadag [at] isikun [dot] edu [dot] tr
• pelin.bolca [at] polito [dot] it
• farukcan.unal [at] yeditepe [dot] edu [dot] tr
• naylor.vilasboas [at] fau [dot] ufrj [dot] br

marking the subject as: IxD&A special issue on ‘Teaching Design After Generative AI’’‘.