Using AI for research: Best tools, benefits, risks, and best practices
In 2025, so much of how we live and work looks different. And we have AI to thank for that. AI has changed how we create new ideas, make decisions, and even how we find information!
What used to take hours of digging can now be done in minutes with the right tools. But how do you actually use AI to make research smarter, faster, and more reliable?
In this post, we’ll break it down: from the benefits and risks of AI research to the best tools and expert tips on using them responsibly.AI is reshaping research one task at a time.By Manar – Reading time: 7 min.
Artificial intelligence has made its way into research across various industries. This is largely due to the potential of AI tools to save researchers time and resources, enabling faster information processing and improved work quality. Here are some key applications of AI in research today:
Literature review and summarization: Using natural language processing and machine learning, AI research tools can scan thousands of papers, highlight relevant findings, and generate concise summaries.
Data Analysis: Analyzing data is at the heart of research, involving the systematic cleaning, transforming, inspecting, and modelling of data to uncover insights and patterns. AI tools can help take on this heavy lifting.
Drug discovery and clinical research: AI is playing a key role in medicine, from predicting protein structures to repurposing existing drugs and improving clinical trial design. In fact, the 2024 Nobel Prize in Chemistry recognized researchers who applied AI to protein structure prediction, highlighting its growing role in biomedical discovery.
Writing and structuring: Studies show researchers are using AI to assist with drafting manuscripts, organizing content, and editing text, making the writing process more efficient.
Environmental and biodiversity research: AI is helping catalog species and analyze ecosystems. The Natural History Museum in London is using it to digitize millions of specimens, while apps like Merlin Bird ID can identify birds by sight or sound, boosting conservation efforts.
The integration of AI in research can have some very promising results. Some of the biggest benefits include:
It saves time. AI can take on repetitive tasks, saving researchers hours of work. A 2025 study published in “Results in Engineering” found that automating processes like data analysis and literature screening can significantly improve research efficiency while reducing workload for scientists.
It can handle big data. Processing massive data sets manually can be really overwhelming. AI tools can quickly analyze complex data, spot patterns, and highlight connections that would be difficult for researchers to find on their own.
It improves accessibility. AI can help break down complex concepts, making research easier to understand and more approachable for a wider audience, including non-specialists.
It frees up creativity. With AI handling routine tasks, researchers have more time to focus on innovation, analysis, and generating new ideas.
Not all AI tools are made equal. Some are designed to help with brainstorming and summarizing, while others are better at digging through academic papers or analyzing complex datasets. Here are five of the most useful ones for researchers today:
Elicit: This AI tool is built to assist with searching and analyzing academic papers. It can be particularly useful for literature reviews, helping researchers quickly identify relevant studies, extract data, and summarize findings.
Semantic Scholar: One of many AI search engines, Semantic Scholar offers intelligent filtering, citation-based recommendations, and summary insights to help researchers find relevant literature quickly.
Scite: Anyone who’s written a research paper knows the pain of citations, and citing a source that isn’t credible can weaken your entire argument. Scite helps by showing whether a paper has been supported, disputed, or simply mentioned in later research, so you can quickly measure its reliability.
Research Rabbit: A visual tool for exploring research papers and citation networks. It lets you create collections of studies, see how they connect, and discover related work. Think of it almost like building your own interactive research map.
Grammarly: More than just a spell-checker, Grammarly uses AI to improve clarity, tone, and readability in academic writing. It’s especially handy for polishing drafts, catching errors, and making sure your research papers sound professional and easy to follow.
While the use of AI in research opens up new opportunities, it also comes with real risks and limitations that shouldn’t be overlooked. Here are some key challenges:
Bias: AI being unbiased is a common myth. In reality, AI systems learn from existing data, which often carries human biases. AI models can then inadvertently reproduce or even amplify patterns of discrimination, resulting in harmful outcomes. In research, this could mean overlooking certain populations in medical studies, reinforcing stereotypes in social science data, or skewing results when datasets aren’t diverse or representative.
Illusions of understanding: A 2024 paper co-authored by Yale Anthropologist Lisa Messeri warns that although AI might help scientists do more, leaning on it too heavily may actually lead to understanding less. By making complex tasks feel easier, AI can create a false sense of mastery. This means researchers could rely on outputs without fully grasping the underlying methods or insights, which risks weakening genuine expertise and critical thinking.
Data privacy: Large language models are often trained on vast datasets scraped from the internet, sometimes without consent, and these can include personal or sensitive details. In research, this raises concerns about exposing health records, demographics, or other identifiable information, especially since studies have shown AI models can “memorize” and later reproduce parts of their training data.
Environmental harm: The environmental cost of developing and training large language models is significant. Generative AI tools like ChatGPT carry a heavy carbon footprint from powering massive data centers to cooling servers. A growing reliance on these models in research raises sustainability concerns, especially as scientists fine-tune them or run simulations on ever-larger datasets. (To learn more about ChatGPT, check out our full guide.)
Accessibility and skill gaps: Not every researcher has equal access to advanced AI tools or the training to use them responsibly. Well-funded institutions are more likely to benefit, while smaller organizations or researchers in under-resourced regions may be left behind. This uneven access risks widening existing gaps in who can fully take advantage of AI in research.
AI can be a powerful partner in research, but with these risks in mind, using it responsibly is key. Here are a few best practices you can adopt:
Verify, verify, verify! AI is not infallible. So, always double-check results against primary sources or peer-reviewed work before using them in your research to avoid errors or misinterpretations.
Be transparent. If you’ve used AI at any step of your research, make that clear in your methodology or acknowledgments so readers understand how the work was produced.
Use AI as your co-pilot, not pilot. AI is only meant to play a supporting role in research, so don’t treat it as a substitute. Let it handle repetitive or time-consuming tasks, but keep analysis and critical thinking firmly in human hands.
Protect sensitive data. Good, responsible research requires careful ethical considerations. Be mindful of what information you share with AI tools, especially if it involves personal or confidential data, and always follow established privacy and security standards.
Watch out for bias. This goes hand-in-hand with verifying for errors. Always approach AI outputs with a critical eye to ensure your research doesn’t unintentionally reflect skewed perspectives.
Consider the environment. Training and running AI models uses a lot of energy. Opt for efficient tools when possible and be mindful of the environmental footprint when integrating AI into your research workflows.
AI is transforming research by helping us work faster, explore more, and focus on big ideas. But it’s not a replacement for human judgment. Used responsibly, it can be a powerful partner in discovery while keeping research accurate, ethical, and future-ready.
Now that you’re well-versed in how to use AI for research, you might also want to see how it can level up your communication. Luckily, we’ve got a handy explainer on using ChatGPT prompts to write better emails.
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