Mapping the Research Landscape of Generative AI in Art Design Education: A Bibliometric Review Using CiteSpace

Abstract

This bibliometric study systematically maps the integration of generative artificial intelligence (GenAI) into art and design education curricula by analyzing 6,098 peer-reviewed papers indexed in the CNKI and Web of Science (WOS) core databases from 2014 to 2024, employing CiteSpace for co-occurrence and cluster visualization. The findings reveal a pronounced inflection point around 2019, coinciding with the advent of large-scale diffusion and transformer models, after which the field entered a phase of exponential expansion; notably, the annual growth rate of international publications has consistently outpaced domestic outputs, reflecting a faster academic uptake in Western institutions despite China's aggressive AI infrastructure investments. Regarding publication venues, WOS-distributed literature spans a highly dispersed matrix across computer science, educational technology, and human-computer interaction journals, whereas CNKI contributions remain markedly concentrated in higher education and vocational pedagogy periodicals, indicating a domestic emphasis on curricular delivery rather than algorithmic innovation. Institutional leadership is overwhelmingly concentrated in regions with mature AI ecosystems—such as California, the Yangtze River Delta, and the Pearl River Delta—where top-tier Chinese universities have demonstrated publication outputs and citation impacts that achieve international parity. However, collaboration network topology exposes a persistent structural asymmetry: domestic cooperations exhibit low density (density < 0.03) and intense regional clustering, whereas international networks display extensive transcontinental connectivity and robust interdisciplinary bridging. On the practical front, GenAI applications—exemplified by Midjourney, DALL‑E 3, and Stable Diffusion—are revolutionizing digital course resources by enabling rapid visual prototyping, personalized style‑transfer tutorials, and adaptive feedback systems, which collectively enhance instructional throughput. For instance, a controlled pedagogical experiment at the China Academy of Art (2024) reported a 40% reduction in conceptual iteration time among undergraduate product‑design cohorts when using GenAI co‑pilots, while parallel trials at Stanford's d.school indicated that AI‑generated critique prompts improved novice students' composition awareness by 28% over a semester. Concurrently, the literature consistently foregrounds critical risks that temper this optimism. First, originality concerns have escalated, with a 2023 survey of 156 U.S. design educators revealing that 72% perceived increased plagiarism ambiguities tied to AI‑facilitated output. Second, algorithmic bias remains underexplored yet pervasive: empirical audits of popular text‑to‑image models have shown systematic overrepresentation of Western aesthetic canons, threatening to homogenize global design education. Third, academic ethics—ranging from authorship attribution to data privacy in student‑uploaded portfolios—pose unresolved governance dilemmas. Collectively, these results underscore a dual‑edged reality: while GenAI substantially enriches pedagogical infrastructure and cognitive scaffolding in art‑design training, its responsible adoption hinges critically on constructing robust ethical guardrails, debiasing training pipelines, and redefining human‑AI creative symbiosis, all of which demand immediate policy and curricular interventions to prevent technological determinism from overshadowing educational equity and authentic creativity.