Coded dataset of the 230 included studies on generative AI in higher education: Focused scoping review corpus

This dataset contains the full coded corpus of 230 studies included in a focused scoping review on generative artificial intelligence (GenAI) in Higher Education and e-learning. The records were identified through searches in Scopus and Web of Science Core Collection and subsequently deduplicated, screened, and analytically coded in accordance with the review protocol described in the associated manuscript. The dataset combines bibliographic metadata and analytical coding variables for each included study. Bibliographic fields include, among others, title, authors, year, journal, volume, issue, pages, language, abstract, DOI, ISSN, publisher, and database-related identifiers. The analytical coding layer includes variables related to country, type of AI/GenAI, educational purpose, primary and secondary educational purpose, discipline, Higher Education level, study design, normalized methodological design, data type, open access status, and citation count. Some variables preserve the original bibliographic or coding labels, while others provide normalized categories used for the analytical synthesis reported in the manuscript. The corpus covers studies published between 2018 and 2026 in a focused subset of high-visibility journals. The purpose of this dataset is to support transparency, reproducibility, and secondary analysis in research on GenAI in Higher Education. It may be reused for bibliometric, methodological, thematic, or comparative studies, provided that the source is appropriately cited.