Using AI-powered Tools for Literature Reviews

Benefits

• Efficiency of literature discovery: in addition to traditional keyword searching, AI technology uses algorithms to discover similar literature via seed papers and citation chains.
• Ease of searching: AI technology allows for searching using natural language. 
• Combination of free web resources and users' subscriptions: The listed tools allow users to import sources from referencing software like Zotero and EndNote. 

Limitations

• Limitation of literature coverage: most of the listed tools use Semantic Scholar and open repositories as their data source, which retrieves open access literature and may not include sources behind paywalls.
• Uncertainty of search algorithms and full-text extraction technologies.
• AI does not identify good research or bad research, information can be generated based on poor research containing errors.
• Creativity is a fundamental feature of LLM/Generative AI, the logic used to predict text might be different from the evidence-based approach in scientific research.
• The above limitations may affect the comprehensiveness of literature discovered and accuracy of answers generated by these tools.

Suggestions

• One of the best practices for using these tools is to find a few quality articles from library databases or MultiSearch (more information on subject databases can be found in subject guides), and use these articles as seed papers for further exploration.
• Always validate the references and check the reputation of the sources, trusted indexing platforms like Google Scholar, Scopus, Web of Science, CrossRef, Library MultiSearch etc. can be used for validating purposes.
• Only use the content generated by AI tools as a starting point and analyse critically, please refer to Macquarie University’s guidance on the responsible use of Generative AI in research and Using Generative Artificial Intelligence Tools at MQ for more information.