Cambridge Professor tackles AI issue head on through nous and newly-designed tool

But rather than being scared of AI, Professor Matthew Grimes of Cambridge Judge Business School has developed two new AI tools to help scholars – especially junior scholars – get their research published in the top academic journals.

In August 2025 Matthew shared on LinkedIn a link to a new tool he developed – the AI-Powered Management Research Feedback Tool – which offers structured manuscript feedback.

He says: “I built the tool based on my understanding of journals’ peer-review and editorial processes, and how AI should be critiquing manuscripts based on that understanding. The tool does not predict whether a paper will or will not pass a peer-review process, but it does tell you that ‘here are some issues with the paper.’”

In January of this year, Matthew turned again to the question of how AI can devise the compelling and boundary-moving questions and answers that drive human knowledge forward in truly meaningful ways. The answer lies in identifying and solving puzzles.

“Too many research projects start from gaps in the literature rather than genuinely important puzzles,” says Professor Grimes, as such articles are initiated based on a supposed “gap in the literature”.

“There’s usually a reason that such a gap in the literature exists, and it’s because it’s not interesting to fill in that gap,” he says. “I observed that junior scholars were too often spending four years in an effort to get published in a top journal only to answer a very niche question that could yield just a tiny bit of knowledge. The right way to do research in the social sciences is to start with something empirically or theoretically puzzling – a tension in the literature or in practice.”

Like elite professional athletes, top-class academics have long regarded themselves as a rare breed. Just as only a small percentage of football players will make it to the English Premier League, the assumption has long been that only the crème-de-la-crème of scholars have the experience, breadth of knowledge, and empirical or theoretical insight to research and write papers to the standards demanded by the best peer-reviewed research journals.

While artificial intelligence is a long way from replicating Lionel Messi on the football pitch, the rapid improvement of AI has dramatically changed the game for researchers in the social sciences in a very short period of time – and this has become a key area of focus for Matthew, who is Professor of Entrepreneurship and Sustainable Futures at Cambridge Judge Business School.

He adds: “I’ve always been interested in information systems and considered going into IT as a profession but then went into research, so these AI tools are re-awakening a latent passion of mine. While academics often experience efficiencies in knowledge synthesis work within and across literatures over time, such efficiencies can take years to realise.

“Conversely, emerging generative AI tools now increasingly offer capabilities aimed to increase those efficiencies and the pace at which those efficiencies are realised by scholars.”

Beyond efficiencies in research execution, a debate has raged over whether AI can also creatively generate the key questions whose answers push the boundaries of academic research – in sporting parlance, to move the ball – in the way that elite peer-reviewed journals demand in order to publish a research paper.

The tool created from the research, which is free to use with an account on AI provider Claude and can be customised with a paid Claude account, allows authors to upload their manuscript into the tool, which then provides feedback through a so-called canvas broken down into individual areas. These are:-

▪ Theoretical motivation, or what fundamental management phenomenon is investigated and what specific question will be answered by the research.

▪ Primary and secondary literature, assessing what foundations of knowledge already exist.

▪ Theoretical constructs and relationships, or the key concepts driving the analysis.

▪ The empirical research context examines, and why this is well-suited to the particular study.

▪ Research design and analysis.

▪ Findings, including patterns from the data analysis.

▪ How the findings advance understanding and practice.

Since announcing this first tool, it has proved popular far beyond the PhD community and has been accessed nearly 3,000 times.

You can read a longer article via:

https://www.jbs.cam.ac.uk/2026/research-shows-efficiency-of-indias-low-cost-space-missions/