Generative technologies are reshaping creative industries and design workflows. This transformation is rapidly reaching undergraduate, studio-based design programs but the empirical picture remains fragmented with inconsistent reporting. A formal synthesis of the emerging evidence is essential to map its course and support informed decision-making. This evidence review maps how generative AI (GenAI) is being integrated into undergraduate and studio-based design education, synthesizing classroom practices, enabling and constraining conditions, pedagogical framings, and directions for future research. As GenAI tools enter art and design programs at pace, evidence remains fragmented across contexts and methods; consolidating it can guide practice, streamline integration, and help set a focused research agenda for design education. We conducted a PRISMA-ScR–aligned evidence mapping of empirical studies published 2020–2024. Systematic searches in Web of Science and Scopus identified studies of GenAI in design-education settings that reported learner, process, or artefact outcomes. After screening, we charted context, use case, and outcomes and produced a narrative synthesis; given the heterogeneity of designs and measures, we did not conduct a formal risk-of-bias assessment. The review contributes a design-education–specific evidence map, classifying GenAI use cases, cataloguing adoption enablers and barriers, and outlining a practical agenda for assessment and study design. Findings show GenAI is being embedded across the design process for ideation, iteration, visualization, and augmented critique. Adoption is supported by access to tools, staff development, and task scaffolds aligned with intended outcomes and rubrics; it is hindered by limited capacity building, curricular rigidity, and policy uncertainty. Few studies make explicit use of educational theory, and inconsistent measures, protocols, and reporting constrain effect estimation. Implications for practice include scaffolding prompting within briefs, aligning outcomes and rubrics with AI-supported processes, requiring disclosure of AI use and process evidence, and ensuring reliable access and support. Implications for research include employing validated measures, comparative and longitudinal designs, and richer reporting of context, tasks, and measures while grounding empirical work in established educational theory. Broader impact lies in encouraging responsible, effective GenAI use in creative education and supporting graduate readiness for AI-mediated design work. Priorities for future research include co-creative workflows and learner experiences, governance and authorship norms, fair AI-assisted assessment that balances the subjectivity of design with automated outputs, longitudinal effects on creative autonomy and artefact quality, and robust frameworks to scale integration.
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