Global Dataset of Peer-Reviewed Studies on Artificial Intelligence for Product Design and Development

This dataset documents the global development of research on AI for Product Design and Development. It focuses on peer-reviewed studies published between 2021 and 2025. The dataset captures the trajectory of scholarly work that investigates how artificial intelligence supports design exploration, product configuration, prototyping, optimization, and decision making in engineering and creative industries. The dataset was curated using a Systematic Literature Review guided by the PRISMA protocol, ensuring transparency and rigor in the screening and selection process. A total of 39 peer-reviewed papers were identified from the Scopus database.Each entry preserves complete bibliographic and metadata fields to enable secondary analysis and reproducibility. The dataset includes author full names, author IDs, paper titles, publication years, source titles, volume and issue information, article numbers, and page ranges. It also stores citation counts, DOI links, and institutional affiliations. Additional fields capture authors with affiliations, abstracts, author keywords, index keywords, and reference lists. The dataset also records correspondence addresses, editors, publisher information, ISSN, ISBN, CODEN, PubMed ID, and the language of the document. It includes the abbreviated source title, document type, publication stage, open-access status, source, and the EID for each record.The dataset is provided in CSV format to support flexibility in data cleaning, preprocessing, and integration with diverse analytical tools. It offers a compact but rich foundation for mapping research trends, identifying conceptual gaps, and examining how AI technologies influence the evolution of product design and development. This dataset aims to support future studies in entrepreneurship, design research, and technology-driven product innovation, particularly in domains where AI continues to reshape creative and engineering workflows.