Assessing Metadata Quality: Analysis of Bibliographic Entries in Librarianship Literature Generated by ChatGPT-5

Generative artificial intelligence (GenAI) models such as ChatGPT are increasingly used in academic contexts, yet concerns persist regarding the accuracy of their bibliographic outputs. With the release of GPT-5, OpenAI claims improved factual grounding and reduced hallucination. This study aimed to assess the accuracy, completeness, and error patterns of bibliographic entries generated by ChatGPT-5 within librarianship literature. Employing an evaluative research design, 200 bibliographic entries were generated using GPT-5 and systematically cross-verified against authoritative sources, including Google Scholar and publishers’ websites. Entries were categorized as correct, partially correct (DOI errors only), or incorrect (multiple metadata errors). Quantitative analysis using descriptive statistics and chi-square tests was complemented by qualitative categorization of error trends. Findings revealed that 74% of entries were fully accurate, 20.5% had incorrect DOIs, and 4% contained multiple metadata errors. While core metadata elements such as author, title, year, and journal were consistently present, critical details such as volume, issue, page numbers, and valid DOIs were often incomplete or inaccurate. Statistical results confirmed significant associations between metadata completeness, DOI presence, and bibliographic accuracy. The study concludes that GPT-5 shows meaningful improvement over earlier versions but cannot yet replace human verification in bibliographic work.