Introduction
Conducting scientific research is one of the most important—yet complex and challenging—parts of academic work. From idea generation and literature review through research conduct, collection and analysis of data to the publication, the journey can be daunting.
Each stage introduces its own set of challenges, and you’re not alone in facing them. Whether you’re a seasoned academic or a student researcher, the uncertainty is always there. Nowadays, artificial intelligence is emerging as a powerful ally. AI-based research tools can act like tireless, knowledgeable and always enthusiastic research buddies—helping you navigate the research process with greater efficiency.
These tools, powered by machine learning and natural language processing, can support almost every phase of research. When used responsibly, they can boost productivity and free up time for deeper critical thinking. But they are not perfect. AI still makes mistakes, and researchers must remain vigilant, using their own expertise to validate outputs.
With the rapid growth of AI tools tailored for academia, it can be hard to know where to begin. This blog post highlights a selection of tools available as of June 2025, categorized by a research tasks they can support.
Brainstorming Ideas
When starting a new research project it is useful to brainstorm ideas and conduct an initial overview of the topic. These tools pull insights from various sources—including scientific articles, industry reports, and news:
- Google Gemini
- Perplexity
- Claude
- Heuristica
- Lateral.AI
- ChatGPT Deep research
Scientific Literature Review
When you need help with selecting scientific literature that in future will transform into the Introduction and Literature Review of your article, these tools can be useful.
Elicit, Consesus, Undermind, SciSpace, Ai2 Scholar QA and many other AI-based assistants use Semantic Scholar as a database of scientific literature for their analysis. As the company explains: “We index over 227 million academic papers sourced from publisher partnerships, data providers, and web crawls”. I think it is a useful tool, but as with all things AI you need to be careful and quality check the sources selected by it. I would recommend using sources that are indexed in Scopus, Web of Science, and national lists of recommended journals, book publishers and conference providers. More on the Semantic Scholar in later posts.
Chat with Articles
Once you have collected articles, use these tools to interact with them. You can chat with your articles and get insights into methodology, research methods, data collection and analytical tools used by other researchers in your field. It can also be useful to discover literature gaps and ideas for future research.
- SciSpace Deep Review
- Thesis AI
- OpenRead
- Explainpaper
- PaperBrain
- Scholarcy
- AnswerThis
- Petal.ai
- Avidnote
- Notebook LM
Writing and Editing Assistance
There comes a point in the research process when you can no longer postpone the actual writing of the research article. When you find yourself at that point these tools can be helpful with creating outline of the article, writing assistance and editing. As with each step of the research process the key is to use AI ethically, as a support and not a replacement of the writer. Check with your institution and potential publishing house on their rules regarding the usage of AI.
- SciSpace Deep Review
- Avidnote
- Jenni
- ChatGPT (in chat type “use canvas”)
- Yomu AI
- PaperPal
- Thesify
- CoeditorAI
Data Visualization Tools
More and more tools appear that can help not only with generation of text but also analysis of data. I think it is still an underdeveloped feature of AI assistants that can also create data privacy issues. Nevertheless, there are few tools that can help with visualization of your data.
Mapping the Literature
When you want to explore your topic more in-depth and discover connections between papers, authors, and research trends these tools can help:
Mapping of literature is usually based on the reference and citation analysis of papers. That is why it is crucial to take into account the source of data that is used by AI-based research assistants. More on this in later posts.
Final Thoughts
AI-based tools will not replace your critical thinking or research expertise—but they can certainly enhance it. From idea generation to publication, these digital assistants can simplify time-consuming tasks and uncover valuable insights. Treat them as research companions, not replacements. Embrace the exciting leap in technology with curiosity and openness, but remain critical and ethical in how you apply AI within academic settings.
Disclaimer: This blog post was developed with the assistance of ChatGPT, an AI language model by OpenAI. While AI contributed to drafting and editing, all content has been reviewed and finalized by the author to ensure accuracy and integrity.
