Recently developed AI research tools can be used to identify resources on a topic, or identify resources that are similar, or linked in some way, to already identified sources. When deciding whether or not to use these tools it's important to:

• Check with your supervisor or lecturer as to whether or not it is allowed and/or appropriate to use these tools for your literature review or assessment.
• Read Your guide to generative AI.
• Keep in mind that these tools have limitations and biases.
• Use these tools with transparency.
• Always evaluate the quality of any information you have found.

Tips:

• Use these tools in conjunction with other search tools, such as journal databases, library catalogues, and citation databases.
• Check out the Library's Generative AI at University: For Research Guide for more advice on using AI tools ethically and responsibly.
• A great starting point is Primo's AI Research Assistant which will link to resources in the Library's catalogue. 

Read:

Some examples of AI research tools are listed below:

Please Note: Examples below are not recommendations but are provided for your information. Please use with care and evaluate all resources found via these tools. 

All the large language models are great tools to use in your research. 

ResearchRabbit

• A citation-based literature mapping tool.
• Add relevant seed papers to a collection to receive recommendations for related, or connected, papers.
• Simple keyword search function also provided.
• Free to use, login with a free account.

Semantic Scholar

• AI-powered academic search and discovery tool.
• AI algorithms assist in discovering hidden connections and links between research topics.
• New Semantic Reader application contextualises citations and improves accessibility among other features. 
• Free to use, login with a free account.

SciSpace

• A research assistant that provides multiple functionalities in assist in various parts of researching and literature reviews
• Uses semantic searching to find and summarise literature
• Provides research organisation tools to save notebooks
• Free version

Elicit

• AI tool that finds articles, summarises abstracts and extracts key information from the article.
• Searches across 200 million academic papers from Semantic Scholar.
• Helps brainstorm your research question.
• Useful to find seed papers for your literature review or to check if you have missed anything after searching the databases.
• Pay-as-you-go service once free trial credits have been used.

Connected Papers

• Creates a graph that maps literature related to an inputted paper.
• Input using an existing article, or conduct a topic search to locate an article.
• Search engine is powered by Semantic Scholar.
• Free tier (offering 5 graphs per month) available.

Consensus

• An AI search engine for researchers. 
• Designed to streamline the literature review process by quickly summarising research findings, identifying the scientific consensus on a topic, highlighting gaps in the literature and providing citations for all insights.

Journal Articles

Bernard, N., Sagawa Jr, Y., Bier, N., Lihoreau, T., Pazart, L., & Tannou, T. (2025). Using artificial intelligence for systematic review: The example of elicit. BMC Medical Research Methodology, 25(1), 75.  https://doi.org/10.1186/s12874-025-02528-y 

Clark, J., Barton, B., Albarqouni, L., Byambasuren, O., Jowsey, T., Keogh, J., Liang, T., Moro, C., O’Neill, H., & Jones, M. (2025). Generative artificial intelligence use in evidence synthesis: A systematic review. Research Synthesis Methods, 16(4), 601-619.  https://doi.org/10.1017/rsm.2025.16  

Eacersall, D., Pretorius, L., Smirnov, I., Spray, E., Illingworth, S., Chugh, R., Strydom, S., Stratton-Maher, D., Simmons, J., Jennings, I., Roux, R., Kamrowski, R., Downie, A., Thong, C. L., & Howell, K. A. (2024). Navigating ethical challenges in generative AI-enhanced research: The ETHICAL framework for responsible generative AI use. ArXiv Preprint, 1-29. https://arxiv.org/abs/2501.09021   

Fabiano, N., Gupta, A., Bhambra, N., Luu, B., Wong, S., Maaz, M., Fiedorowicz, J. G., Smith, A. L., & Solmi, M. (2024). How to optimize the systematic review process using AI tools. JCPP Advances, 4(2), e12234.  https://doi.org/10.1002/jcv2.12234 

Gartlehner, G., Kahwati, L., Hilscher, R., Thomas, I., Kugley, S., Crotty, K., Viswanathan, M., Nussbaumer-Streit, B., Booth, G., Erskine, N., Konet, A., & Chew, R. (2024). Data extraction for evidence synthesis using a large language model: A proof-of-concept study. Research synthesis methods, 15(4), 576–589. https://doi.org/10.1002/jrsm.1710

Ge, L., Agrawal, R., Singer, M., Kannapiran, P., De Castro Molina, J. A., Teow, K. L., Yap, C. W., & Abisheganaden, J. A. (2024). Leveraging artificial intelligence to enhance systematic reviews in health research: Advanced tools and challenges. Systematic Reviews, 13(1), 269.  https://doi.org/10.1186/s13643-024-02682-2  

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