From maker to curator: a scoping review of generative artificial intelligence in design higher education
DOI:
https://doi.org/10.31812/ed.1093Keywords:
generative artificial intelligence, design education, scoping review, higher education, creativity, design pedagogy, TPACK, human-AI collaborationAbstract
Background: The rapid proliferation of generative artificial intelligence (GenAI), particularly following ChatGPT's release in November 2022, has created unprecedented opportunities and challenges for design education. Despite intense scholarly interest, no comprehensive synthesis maps how AI is being conceptualized, implemented, and researched across design disciplines in higher education.
Objectives: This scoping review maps the extent, nature, and characteristics of research on generative AI in design education, examining pedagogical approaches, reported outcomes and challenges, and identifying gaps requiring future investigation.
Eligibility criteria: Studies addressing AI or generative AI technologies within design disciplines (architecture, fashion, graphic, product, UX/UI, interaction, or general design education) in higher education contexts, published in English between 2017-2026.
Sources of evidence: Web of Science Core Collection searched using 18 query combinations of design education and AI-related terms.
Charting methods: Data extracted using a standardized form capturing 21 variables across bibliometric, methodological, technological, and pedagogical dimensions.
Results: From 1800 initial records, 156 studies met inclusion criteria. Publication volume increased 6.4-fold following ChatGPT's release (86.5% post-2022). China (31.4%) and the USA (13.5%) dominate geographically. Research concentrates on creativity development (35.9%), assessment (27.6%), and curriculum design (22.4%). Only 26.3% employed theoretical frameworks, predominantly TPACK, design thinking, and experiential learning. Significant gaps exist in outcome reporting (66.7% unspecified), challenge documentation (74.4% unspecified), and methodological rigor (53.8% lower-quality designs).
Conclusions: While research on generative AI in design education has surged, the field remains theoretically underdeveloped and methodologically nascent. We propose a research agenda prioritizing longitudinal studies, theoretical integration, under-researched disciplines (particularly graphic design), and ethical frameworks. The emerging paradigm shift from "maker" to "curator" demands reconceptualization of design education's foundational pedagogies.
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