Search Smarter: AI as a Research Assistant

Individual sections of this blog post were updated in January 2026.

Literature research is an important part of any academic work. The rise of artificial intelligence has opened new ways to make the search for relevant literature more efficient. But which AI tools are suitable for scientific research and how do they differ from traditional databases? In this article, we show how AI can be used throughout the individual steps of literature research – from finding a topic to deepening your research – and what you should definitely pay attention to in the process.

What is Artificial Intelligence & Generative AI?

Artificial intelligence (AI) refers to systems or machines that perform tasks that would normally require human intelligence, such as data analysis, pattern recognition or text generation. However, in order to use AI tools effectively for literature research, it is important to understand how these systems work and what their limitations are.

Well-known tools such as ChatGPT, Google Gemini and Microsoft Copilot belong to what is known as generative AI. They are based on large language models (LLMs), which function as highly sophisticated text prediction systems. Unlike traditional databases, these models do not draw on verified knowledge bases, but calculate probabilities of word sequences based on their training data. They therefore do not actually ‘know’ anything, but generate texts that sound plausible. For this reason, such tools are ideal for brainstorming, structuring and linguistic support. However, they are often unreliable when it comes to precise source discovery or verification, as they often generate unreliable or even fictitious source references. But there are now specialised AI tools that offer new search approaches and are a useful addition to library search engines and literature databases.

How can I use AI for literature research?

Even though ChatGPT & Co are not traditional research tools, they can provide useful support at various stages of the literature research process. AI can help with brainstorming, preparing and carrying out literature research - but also with understanding the text. In the following, we will focus on the typical process of scientific literature research and show which AI tools can be used effectively in which phases. It is important to note that the results generated by AI must always be critically examined and compared with your own expertise and the current state of research.

Finding a topic

The first step in any research project is choosing a suitable topic. AI tools such as ChatGPT, Google Gemini or Microsoft's Copilot can provide valuable support here. A possible prompt could be: ‘I want to write a scientific paper on [...]. Can you help me narrow down the topic and identify current trends?’

The ‘Deep Research’ function, which is now integrated into tools such as Perplexity, ChatGPT, Gemini and Mistral, is particularly powerful in this phase. In contrast to simple text generation, these systems follow a ‘search-analyse-write’ cycle: they search the live web in real time, evaluate various sources and use them to create a structured report including initial source references. These results are useful for guidance purposes, but are no substitute for systematic literature research.

Prepare the literature search

Once you have gained an overview of the topic, the next step is to conduct a literature search. ChatGPT and the like can help by generating search terms, creating search strings or recommending suitable databases for the search. Here it is important to identify synonyms, generic terms, sub-terms and related terms in order to carry out an effective search in databases. By combining AI-generated search terms and traditional search methods such as the use of Boolean operators, you can optimise your search results.

Prompt: ‘Create a literature search for the research question [...]. Please identify key terms related to the topic. Find synonyms and related terms and present them in a table. Truncate the terms in the next step. Create meaningful search strings for the literature search in databases.’ (See HLB RheinMain. KI-Tools für die Recherchevorbereitung. https://www.hs-rm.de/hlb/suchen-entdecken/ki-tools/zur-recherchevorbereitung [Zugriff: 18.07.2025])

Although these workflows can be well supported by AI, the technical control of the terms always lies with the user. You can now insert the created search string into the search in databases such as Web of Science or Scopus. Pay attention to the help page of the individual databases, as certain search operators may not be supported.

Conduct a literature search

While AI primarily plays a structuring role in the preparation phase, it is used directly in the next phase, the actual literature search. Specialised AI tools play an important role as soon as a targeted search for peer-reviewed articles begins. These tools differ fundamentally from generative AI, as they are directly linked to scientific databases.

Specialised AI tools for literature research, such as Scite, Semantic Scholar or Consensus, combine searches in scientific databases with AI techniques such as natural language processing (NLP) or semantic search. This enables these tools to better understand the context of a search query and process large amounts of data more quickly. These tools access scientific databases and take into account citable sources. Not only can they identify relevant articles, they can also provide concise summaries and visual representations.

Find similar literature

To deepen your literature search, you can use the so-called snowball effect. There are also suitable AI tools for this step, such as Connected Papers. Connected Papers is a literature mapping tool that helps you to explore scientific papers and their connections to other papers. It generates a visual map of scientific articles based on a ‘seed paper’ and shows their relationships to each other. These relationships are determined by similarities in the citations and content of the papers.

We have only presented a small selection of tools here as examples. You can find more AI tools and their functions in this overview.

Challenges: What you need to look out for in AI-supported research

Unclear database

The database of many AI tools is often unclear and the providers disclose little to no information about it. The models often refer to publicly accessible content, especially open access content, while licenced or fee-based scientific databases are generally not taken into account. 

Danger of misinformation (hallucinations)

Another problem with the use of AI tools is the risk of hallucinations - false or invented information that can appear to be reliable information. AI models often use abstracts and metadata such as titles and authors to generate content. However, these abstracts can only provide superficial information, and important details or contextual aspects are lost.

Therefore, it is crucial to always check citations and references to ensure that the information provided is correct. If an AI tool does not display full texts, access to the relevant sources should be ensured via library access or interlibrary loan.

Costs and subscriptions

Many AI tools only offer a limited basic version of their services free of charge. Full access to advanced functions and reliable data usually requires a paid subscription. Users should weigh up whether the services offered are worth the price and which functions are really necessary for their own research.

Data protection

Another important aspect is data protection, especially for tools based outside the EU. If registration is required, it is important to check how personal data is processed and what risks are involved.

Conclusion

AI tools are a valuable addition to traditional literature research. They can speed up the research process and open up new perspectives. Nevertheless, traditional literature research in scientific databases remains indispensable, as these provide access to reliable, citable and quality-assured sources. A conscious and critical use of both methods enables well-founded and comprehensive research. Always check that the sources you find are correct and citable and remember good scientific practice.

Additional resources

Bucher, Holzweißig, Schwarzer, Holzweißig, Kai, Schwarzer, Markus, & Verlag Franz Vahlen. (2024). Künstliche Intelligenz und wissenschaftliches Arbeiten : ChatGPT & Co.: der Turbo für ein erfolgreiches Studium. Also available as E-Book: https://permalink.obvsg.at/tug/AC17101632

Lahrsow, Miriam. (2025). KI-Tools für die wissenschaftliche Literaturrecherche: Potenziale, Problematiken, Didaktik und Zukunftsperspektiven. Bibliothek Forschung und Praxis. https://doi.org/10.1515/bfp-2025-0002

Taskcards AI-Tools for Literature Search

Overview AI in Literature Search

AI at TU Graz

Parts of the text were revised using DeepL Write and checked and edited by the author. The English translation was created with the help of DeepL Translate.