Insights from a PhD student: Saving time without missing key research

Introduction

David Henkelmann is currently in the first year of his PhD. And with a job as an IT specialist keeping him busy four days a week, optimizing his study time is important. Yet, like most researchers, it’s a challenge to find the hours required: “Gathering the pile of relevant information to read through is the most difficult and tiring thing because I don’t want to leave anything out,” David explains. “I have this tick that I need to scan all the papers I find — at least the title and the abstract — and that's very time consuming.”

For early career researchers in particular, literature discovery isn’t just about speed, it’s about confidence. Missing a key paper can change how they shape research questions and methodologies — it can even impact the viability of projects.

Until recently, David has been turning to the AI capabilities in Scopus for help, on the recommendation of his professor. “He said that ScopusAI is by far his preferred tool for finding literature. And I’ve been very surprised by how well the AI tools in Scopus work, especially for my topic. They are very precise and I like the possibility to chat. I also like the fact that they recommend follow-up queries.”

A few months ago, David was invited to explore LeapSpace, Elsevier’s new AI-assisted workspace. LeapSpace is built to support multiple stages of the research workflow, from exploratory literature review to deeper analysis, funding discovery and collaboration. And because it’s all in one secure environment, it can help researchers work faster, think deeper and achieve more — all without switching tools.

David says: “For me, LeapSpace is a natural extension of the AI capabilities in Scopus. They both put papers in context so that I can understand them. And that helps me build a stronger pile of reading material.”

And he’s already seen benefits. David recently switched to a new PhD topic spanning the fields of informatics and psychology — he is exploring why people post things about themselves on social media. And he estimates that using LeapSpace for his preliminary literature review saved him up to two weeks of work. For PhD students like David, this kind of time savings during the orientation phase can free up space to focus on core tasks like hypothesis building and critical reading. “I really had the feeling I could start reading faster. I would say that it streamlined up to 40% of my questions and literature searches,” he reveals.

Digging deeper with Deep Research

LeapSpace’s Deep Research mode has become David’s preferred starting point when he’s exploring a new subtopic for his PhD. Deep Research uses a multi-agent AI system to generate a comprehensive report, which makes it a great option for complex, interdisciplinary or open questions. And as with every LeapSpace response, claim or assumptions are fully referenced and can be traced back to their peer-reviewed sources.

Importantly, a Deep Research report doesn’t just seek to answer the user’s question — because it has been designed to mirror how researchers are trained to think, it surfaces valuable insights that provide context and help the user continue exploring. For example, Deep Research surfaces evidence boundaries, identifies gaps and encourages critical thinking.

“I really like the way that the Deep Research report is built,” David explains. “You get an easy introduction, which is great for quick reference, along with tables of key findings and a direct answer to your question. Then you get the study scope, and assumptions and limitations. And it builds to a conclusion, synthesis of findings and even recommendations for future research.

“And when I use it, I have the feeling that the AI understands what I want – the information is sorted into groups that really grasp the key elements of my question. The answers I get are all more or less relevant, and I’m confident that nothing has been left out.”

He adds: “When I click on a reference in LeapSpace, a panel opens containing the document details and the abstract – that helps me double check that reference and understand why the AI has grouped together particular papers. Details like these are why I use it for my research.”

While David likes the Deep Research report format and content, he would like to see some tweaks: “It would be perfect if there were number ranges I could choose from to show how many references I want featured in a response. I’d also like the option to ask for everything the AI has found.”

Trust signals and transparency

The fact that LeapSpace shows each search step on screen, along with the tools, sources and filters used, has helped to build David’s confidence in the responses he receives. “When you use other AI tools, you need to write your own keyword search chains with the right number of words — not too many, not too few — and specific words can mean different things. That is difficult, but LeapSpace does all that for me. And because it shows me the queries it is building, I can double check them. It’s a really nice touch of transparency and that’s important to me.” This approach echoes the research community’s expectations around reproducibility and methodological transparency in scholarly research.

David was working with LeapSpace prior to the launch of LeapSpace's Trust Card, which he expected to further build his confidence. The Trust Card ‘Link to statement’ shows how closely a claim aligns with the underlying source. “I think it’s going to be very valuable because it saves you time by verifying that the AI and you are on the same path.”

And he was excited about the introduction of the Trust Card feature, Claim Radar, which highlights the proportions of wider published literature that support, contradict or are ambivalent about claims in a LeapSpace response. It also provides representative sources with mini rationales.

“There is an overwhelming amount of literature out there and for every statement they contain, you can find work that agrees and disagrees,” David explains. “Often, I’m looking for a paper that proves a hypothesis I’ve come up with, so to be able to understand which works support it is really helpful. It’s also important to know which papers disagree — and why — because in informatics and psychology, there is no right answer. I don’t want to publish and then discover there were works in the shadows that I missed. Claim Radar will provide that comprehensive overview.”

This can be particularly helpful for researchers working in fields where evidence is mixed or evolving, especially interdisciplinary areas like informatics and psychology. A view of the broader literature landscape allows for stronger, more defensible arguments.

Comprehensive insights in a single location

Alongside literature discovery and exploration, LeapSpace helps with research tasks like finding collaborators and funding, with support for more workflow steps in the pipeline.

With funding for his university position due to expire in June this year, David plans to use LeapSpace to seek out other funding options: “especially the smaller [awards] that are often difficult to find elsewhere.” LeapSpace includes access to more than 36,000 active and recurring funding opportunities worth over $100 billion globally. That means it’s not only designed to make it easier to find major programs, but to surface those smaller, harder-to-find, opportunities too.

He also plans to use the Topical Author Search functionality — a researcher identification tools that searches using vectors. This helps it look beyond exact matches to find the ‘spaces in between’. This could mean relevant experts in adjacent or complementary fields, where terminology differs but underlying concepts overlap, as well as those whose work spans multiple disciplines. It also makes the tool a powerful ally for cross-disciplinary research.

David explains: “Normally, the way I find authors relevant to my research is by looking up people I’ve seen mentioned in three or four papers. But it’s very important to me to not only find people who know about a single topic — I want to find and talk with people who are reaching outside of their bubbles and connecting them to make new research fields. With LeapSpace I can do that. I can also preview that author’s publishing history.”

He adds: “What I need from AI is a partner. LeapSpace is not going to give me all the answers like ChatGPT. LeapSpace feels like a helper that minimizes the work required to gather information — research, the important authors, cross references. And it’s all in one space.”

Another plus point for David is that LeapSpace searches both full-text journal and book content — including full-text journal content from other publishers — as well as millions of abstracts indexed in Scopus.

“Abstracts show me what a paper is about, and full text always contains nuggets that enrich the information. I like the fact that LeapSpace searches both and it would be very helpful to me to have access to full-text content from multiple publishers in a single location — the more data and information the better, in my opinion.” He adds: “And if it’s in LeapSpace, I will have features like the Trust Card and Claim Radar to help me understand it.”

For David, LeapSpace isn’t about replacing judgment - it’s about creating the space to apply it more effectively.