This study aims to evaluate the accuracy and quality of MARC21 catalogue records generated by ChatGPT using bibliographic data from title pages. The study will shed light on the effectiveness and reliability of automated cataloguing processes utilising AI technology. This involves examining factors such as correctness, consistency, and adherence to Resource Description and Access (RDA) standards. The analysis highlights variations in error rates across different records. Identifying the underlying causes of errors in records with higher rates can help in implementing targeted improvements to enhance the data quality and consistency of ChatGPT-generated catalogue records.
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Niveditha et al. (2024) studied this question.
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