If you’ve been wondering whether artificial intelligence is actually useful for research, or whether it’s just hype, the short answer is: it’s real, it’s here, and it’s moving fast.
We recently sat down with Alok Sahu, Senior Research Software Engineer at the AI Competency Centre, University of Oxford, to talk through what’s changed, what’s possible, and what researchers should be doing about it.
Why are LLMs Relevant to Research and Why Now?
Researchers have always worked with considerable volumes of text, reading papers, writing them, taking notes, reviewing others’ work. So, tools that handle text more efficiently have always had direct relevance. What’s shifted dramatically in the last couple of years isn’t the idea of using AI, it’s the quality of what these models can actually now do. Where they once struggled beyond a simple sentence completion, they can now summarise multiple long documents, draft and debug code, and carry out complex multi-step tasks with limited supervision.
The Agentic Leap
One of the most significant recent developments is agentic AI – models that don’t just generate text but take actions. They can run literature searches, extract data from research papers, search the web, and return results, all in one workflow. For researchers, this opens up possibilities that simply weren’t practical before.
It’s Not Just for One Field
The applications span multiple industries and sectors. In agriculture, Alok discussed with us, how AI is being used to predict crop yields and monitor fields. In construction, it’s flagging unsafe worksite behaviour and running design simulations. In healthcare, specialist models are emerging that can assist with drug discovery and genetic analysis. Whatever your research domain, there’s likely something relevant already developing.
Where to Start
Alok’s advice is straightforward: dive in. Try the tools, experiment with your prompts, and see how the outputs change. Beyond the general platforms like ChatGPT, Claude, and Gemini, it’s worth exploring whether specialist models exist in your own field. The pace of development means domain-specific tools are emerging all the time.
The gap between those engaging with AI and those who aren’t is widening. The good news is it’s not too late to start and there’s no better time than now.