The New Zealand-based research platform has updated its literature discovery tools to encourage deeper exploration while helping graduate students return to their original research questions
ResearchRabbit has introduced Analyse and updated its literature discovery platform to help graduate students explore connections between academic papers while maintaining a route back to their original research questions. Image credit: ResearchRabbit.
ResearchRabbit has introduced Analyse, a new addition to its academic literature discovery platform, alongside updates designed to help graduate students follow unexpected research connections without losing track of their original questions.
The changes are intended to support exploratory literature reviews, allowing researchers to investigate related findings and ideas that emerge during their searches.
Based in Wellington, New Zealand, ResearchRabbit uses algorithms to map citation networks across a database of more than 320 million academic papers. The company says 4.5 million people have used the platform since its launch in August 2021.
Unlike general-purpose AI assistants such as ChatGPT, ResearchRabbit does not currently use generative AI. Instead, its recommendations are based on citation networks and the papers selected by individual researchers, with the system adapting to their reading patterns and research interests.
Analyse encourages researchers to look beyond their original questions
The new Analyse addition is designed to encourage researchers to investigate ideas at the edges of their existing work, potentially identifying connections between subjects or findings they might otherwise overlook.
The wider update also seeks to address a familiar problem in academic research: following one interesting paper into another can uncover useful material, but it can also make it difficult to return to the original research question.
Associate Professor Mahsa McCauley, Head of Computer and Information Sciences at Auckland University of Technology (AUT), argues that this type of exploration should form part of the research process.
“The literature review is where it starts. It shouldn't be a box-ticking summary of what's already known. It's the place to wander, follow the threads that surprise you, and come back with a sharper question than the one you started with,” she says.
ResearchRabbit's approach differs from tools primarily designed to generate answers or summaries. Its visual interface allows researchers to examine relationships between papers, authors and concepts, while its recommendation algorithms respond to the material they choose to explore.
Digl Dixon, Head of Product at ResearchRabbit, explains: “We aren't building a tool that generates answers. Answers are a termination point; rushing to answers endangers creativity, curiosity, and deep exploration.”
The company has not provided detailed technical specifications for Analyse or comparative data demonstrating how the updates affect research efficiency or literature review quality.
AI adoption is changing how researchers discover academic literature
The update arrives as AI tools become increasingly common in academic research. The AI for Science 2026 study, compiled by Fudan University, the Shanghai Academy of AI for Science and Nature Research Intelligence, draws on 10,480 qualified survey responses from researchers across 117 countries or economies.
McCauley sees the changing relationship between AI and academic research as an opportunity to place greater emphasis on developing questions rather than simply retrieving answers.
“Now AI can produce answers in seconds, so the real skill is knowing which questions are worth asking,” she says.
ResearchRabbit remains available through a Free Forever tier, with paid access also offered. Its latest updates continue the company's citation-based approach to academic discovery rather than introducing generative AI into the platform.
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