Research Atlas of Generative Artificial Intelligence in Design Teaching: A Bibliometric and Systematic Review Based on CiteSpace

With the rapid expansion of generative artificial intelligence (AIGC) in education, its use in design education shows dual potential for enhancing creativity and transforming teaching practice.However, systematic reviews that clarify its evolution and key challenges remain scarce.To address this gap, this study conducts a bibliometric analysis using systematic literature screening and CiteSpace, based on data from the Web of Science database.After multiple screening rounds, 204 English-language articles (2021-2025) were analyzed to map the research landscape and developmental trends of AIGC in design education across four dimensions: publication trends, keyword co-occurrence, thematic clustering, and temporal evolution.The results show that interest in this field has grown significantly since 2023, with research themes evolving around the progression from technology adoption to teaching design, creative expression, and learning assessment.Keyword co-occurrence analysis highlights "creativity" and "learning outcome" as central nodes in the knowledge network.Thematic clusters align with core educational technology theories, such as TAM, TPACK, and Bloom's Taxonomy, illustrating how AIGC supports multiple layers of teaching innovation.Furthermore, burst detection and trend tracking reveal that keywords like "ethics" and "student creativity" have gained rapid traction, signaling a shift from tool usage to broader concerns around institutional frameworks and educational governance.While the study offers a valuable overview of the field's development and future directions, its scope is limited by the use of a single database and the interpretive limits of semantic analysis.Future studies should incorporate multilingual sources and supplement findings with qualitative approaches to deepen insight.Overall, the findings offer empirical support and theoretical guidance for future research, curriculum development, and educational policymaking.

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Publication Details

Journal
Asia-pacific Journal of Convergent Research Interchange
Published
2025-08-29
DOI
https://doi.org/10.47116/apjcri.2025.08.03
Primary Topic
Artificial Intelligence Applications
Type
article
Field-Weighted Citation Impact
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article

Research Atlas of Generative Artificial Intelligence in Design Teaching: A Bibliometric and Systematic Review Based on CiteSpace

HongYan Ren, Kyeong Seo Kim
Asia-pacific Journal of Convergent Research Interchange
Artificial Intelligence Applications
article

Research Atlas of Generative Artificial Intelligence in Design Teaching: A Bibliometric and Systematic Review Based on CiteSpace

HongYan Ren, Kyeong Seo Kim
article en

Abstract

With the rapid expansion of generative artificial intelligence (AIGC) in education, its use in design education shows dual potential for enhancing creativity and transforming teaching practice.However, systematic reviews that clarify its evolution and key challenges remain scarce.To address this gap, this study conducts a bibliometric analysis using systematic literature screening and CiteSpace, based on data from the Web of Science database.After multiple screening rounds, 204 English-language articles (2021-2025) were analyzed to map the research landscape and developmental trends of AIGC in design education across four dimensions: publication trends, keyword co-occurrence, thematic clustering, and temporal evolution.The results show that interest in this field has grown significantly since 2023, with research themes evolving around the progression from technology adoption to teaching design, creative expression, and learning assessment.Keyword co-occurrence analysis highlights "creativity" and "learning outcome" as central nodes in the knowledge network.Thematic clusters align with core educational technology theories, such as TAM, TPACK, and Bloom's Taxonomy, illustrating how AIGC supports multiple layers of teaching innovation.Furthermore, burst detection and trend tracking reveal that keywords like "ethics" and "student creativity" have gained rapid traction, signaling a shift from tool usage to broader concerns around institutional frameworks and educational governance.While the study offers a valuable overview of the field's development and future directions, its scope is limited by the use of a single database and the interpretive limits of semantic analysis.Future studies should incorporate multilingual sources and supplement findings with qualitative approaches to deepen insight.Overall, the findings offer empirical support and theoretical guidance for future research, curriculum development, and educational policymaking.

Asia-pacific Journal of Convergent Research InterchangeVol. 11(8)
Openalex Percentile: Top 88%
Artificial Intelligence Applications
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Research Atlas of Generative Artificial Intelligence in Design Teaching: A Bibliometric and Systematic Review Based on CiteSpace — HongYan Ren, Kyeong Seo Kim · Asia-pacific Journal of Convergent Research Interchange (2025) | TGRS Research Map | TGRS