Artificial Intelligence (AI) and the literature review process: Tools
On this page we review the benefits of using Ai for researching, list AI academic Research Tools used by researchers when conducting a literature review and discuss about generic AI chatbots.
Benefits of using AI for researching
Generative AI tools have benefited doctoral research students in three key ways (Kumar and Gunn, 2024):
- Facilitating discovery and connections - helping you to discover key works and researchers and to see how these are connected. Tools also help to identify themes and trends.
- Increasing efficiency - saving you time by identifying highly-cited articles and often summarising them, enabling you to prioritise your reading.
- Making the process less intimidating - providing overviews of a topic, breaking down the literature and presenting complex information in a more accessible way helps to ensure you are not overwhelmed by the literature.
Generic AI Chatbots
AI companies initially adopted a chat interface to allow the public to interact with language models from a Web browser. The intuitive interface, the ability to generate human-like answers to questions posed using natural language, rapid response times and the high accuracy of the responses has encouraged high use of these chatbots by students.
A survey in Spring 2023 of 5894 Swedish university students found over half (55.9%) had a positive attitude towards their use but a similar number (54.2%) expressed concern about their future impact on students' learning (Stohr et al., 2024).
Uses of AI chatbots
Generative AI chatbots can help with research tasks by generating article or content summaries, by giving you information about a topic, by suggesting areas for further research and by helping you to analyse and structure the material. They can be used with speech-to-text or text-to-speech software to help students with visual impairment. They can assist in the development of language skills for international students and can assist with programming, report writing and project management skills. They have the potential to provide a wide range of benefits and opportunities for students (Kasneci et al., 2023).
Which chatbot is the best?
In the extremely competitive field of generative AI, it can be difficult to choose an AI chatbot over another. Often you will not have a choice: the chatbot is supplied with your smartphone. For example, Google's Pixel 9 series is supplied with Gemini built in; Galaxy AI is the research assistant with Samsung's Galaxy smartphones; Apple Intelligence is supplied with the latest versions of the iPhone.
The capability of an AI chat bot depends primarily on the LLM models it relies upon, and we can use standardised tests developed by AI companies to test LLMs performance. UC Berkeley researchers created the Chatbot Arena LLM Leaderboard to rank LLMs based on the task at hand such as language, vision, text to image, programming or price. There are other important aspects you should consider when choosing an AI chatbot: privacy, accuracy and the moderation level of the generated content. The user interface and the ability to upload files or to customise a chatbot determine its practical usefulness.
Below, are links to popular AI chatbots:
- Chat GPT (Open AI)
- Gemini (Google)
- Claude.ai (Anthropic)
- Le Chat (Mistral)
- Copilot (Microsoft)
- Perplexity (Perplexity AI, Inc.)
- Pi.ai (Inflection AI)
- HuggingChat (Hugging Face) Choose from multiple open source models such as Meta Llama, Qwen and DeepSeek.
Any information you enter into free AI tools may be disclosed publicly, become the property of the website, or be shared with third parties.
Look for confidentiality notices or consult the terms and conditions or privacy policies before uploading copyrighted or personal materials online.
Academic Research Tools
These tools assist researchers with searching, analysing and visualising academic publications:
Follow the use cases links for guidance and examples applying to conducting a literature review.
Connected Papers
Connected Papers (Smolyansky, 2020) is based on the Semantic Scholar database. Its premise is that two papers that have highly overlapping citations and references are presumed to have a higher chance of treating a related subject matter. The graphs that are produced are designed to highlight the most important and relevant papers immediately. You will only get 5 graphs per month for free.
Use cases: visualising search results, finding similar work.
Limits: You will only get 5 graphs per month for free.
Elicit
Elicit will do this for you saving you time and allowing you to then synthesise the information. Its own user survey found that 10% of respondents said that Elicit saves them 5 or more hours each week and that, in pilot projects, Elicit saved research groups 50% in costs and more than 50% in time by automating data extraction work they previously did manually (Elicit, 2023).
The free basic version allows you to extract data from papers and upload your own papers. However, only priced versions of the product will give you summaries of papers and allow you to extract the information into csv and bib formats.
Use case: Data Extraction.
Inciteful.xyz
Inciteful.xyz has pulled information and/or inspiration from Semantic Scholar but also three other data sources: OpenAlex, CrossRef and OpenCitations. It has used these sources to help researchers get up to speed on a new topic, to find the latest literature or to work out how two ideas are connected (Weishuhn, 2024). There are two tools that are under active development: Paper Discovery and the Literature Connector. Paper Discovery builds a network of papers from citations, analyses the network, and allows you to get up to speed on a topic. by finding the most similar papers, important papers as well as prolific authors and institutions. Literature Connector allows you to enter two papers and it will give you an interactive visualization show you how they are connected by the literature.
Use case: visualising search results.
LitMaps
LitMaps is also based on the Semantic Scholar database. As with the above, it uses the citation network to construct graphs to visualise the research landscape of your topic. Its Discover tool enables you to find gaps in your own literature reviews and to upload existing literature reviews to find not only more recent papers but also papers that the reviews may have missed.
Use cases: visualising search results.
Rayyan AI
Rayyan AI is a web-based automated screening tool, developed by Qatar Computing Research Institute (QCRI), which launched in 2014.
It uses text mining techniques to identify relevant information using statistical pattern learning that recognises patterns in the data.
Use case: Screening.
Research Rabbit
Research Rabbit also uses Semantic Scholar to search for papers but also uses PubMed to find biomedical and life sciences papers. It is committed to remaining free to researchers.
Use cases: visualising search results.
SciteAI
SciteAI, designed by Nicholson et al. (2021), categorizes citations according to whether the articles were contrasting, supporting or mentioning. Scite.ai is very keen for you to take a 7 day free trial but will provide you with a certain amount of free information that you can use for critical evaluation.
Use case: Synthesis.
Semantic Scholar
Semantic Scholar is a free, AI-powered search and discovery tool that helps researchers discover and understand scientific literature most relevant to their work.
Semantic Scholar sources its content via web indexing and from partnerships with scientific journals, indexes, and content providers. These include leading publishers such as Springer Nature, Taylor & Francis, Wiley and Sage and institutions such as the IEEE and ACM. It does not contain content from Elsevier, Emerald or Oxford University Press. There is a list of its publisher partners.
Semantic Scholar does not support Boolean searching or wildcards. Its tutorial pages tell you that Semantic Scholar covers papers across all subjects including biomedicine, computer science, business, history, and economics. Its Ask This Paper and Generative Term Understanding features employ generative AI.
Use cases: Searching.