“Through the looking glass: envisioning new library technologies” academic search using artificial intelligence tools

Purpose This column seeks to interrogate the trend of artificial intelligence (AI)-powered search through the prism of academic search. By focusing on SciSpace, Semantic Scholar, Elicit, Google Scholar, Research Rabbit, PubMed and CAB Abstracts, and providing an analysis of the AI systems’ interfaces and outputs each, the authors aim to provide information professionals with a deeper understanding of the current landscape and help them navigate the complex world of AI-driven AI search. Design/methodology/approach A veterinary medicine topic was selected to test the success of AI tools in searching for academic sources. The authors searched for scholarly literature on the topic, colic AND horses AND microbiome in each of the AI tools (SciSpace, Semantic Scholar, Elicit, Research Rabbit and Google Scholar) and databases (PubMed and CAB Abstracts). For some of the AI tools the authors phrased the search in a question, “What is the relationship between colic and the microbiome in horses?” since they were designed to answer questions rather than execute Boolean searches. The authors downloaded about 100 records from each of the tools and exported them into the reference manager EndNote and then searched for duplicates among all the results, allowing us to calculate the number of references shared by each tool with at least one other tool versus the number of unique references. Findings The authors identified key differences and similarities between the various search tools. Originality/value This overview also demonstrates the variety of ways the same basic technological idea can be implemented. Understanding these specific tools will enable information professionals to use them more effectively, as well as see the larger trends that will continue to develop into the future.