How to Write AI-Powered Literature Reviews: Balancing Speed, Depth, and Breadth in Academic Research
Conducting comprehensive literature reviews often poses challenges for graduate students, requiring extensive reading and synthesis of existing works while advancing original insights. With the rise of academic AI tools, numerous AI-powered literature search engines have emerged. These tools aim to ease difficulties but it can be overwhelming to learn how to use them. This blog cuts through the noise, explaining how these technologies can augment literature reviews and highlighting the unique value different tools provide.
The initial literature search and synthesis process can be exponentially faster with emerging AI-powered tools like Consensus, Scite, Elicit, Research Rabbit, SciSpace, and Litmaps. Whether accelerating paper discovery, evaluating claims, organizing notes, or visualizing connections, responsible AI integration fundamentally elevates literature review workflows rather than replacing human discernment. By clarifying what problems each tool solves best, researchers can selectively incorporate them alongside traditional methods for a more empowered and enriching process that upholds the rigor of this foundational academic skill. Below, we explore how these AI technologies are poised to support the writing of literature reviews by automating tasks to accelerate speed while the researcher (acting as the human in the loop) stays involved to uphold depth and enhance interdisciplinary breadth.
This article starts with a review of why and how these tools are useful before establishing how to approach the process responsibly by maintaining a “human in the loop” to ensure ethical practices.
Accelerating Search Speed
Robust reviews demand extensive and usually lengthy (e.g., dissertations) searches to gather sources. This manual process takes time away from analysis. AI automation rapidly analyzes literature to retrieve relevant sources quickly. For example, Consensus and Litmaps locate papers and “position” them within the broader disciplinary conversation. Consensus answers yes/no research questions using a "Consensus Meter" that analyzes existing literature. It then outputs an overview showing the degree of alignment with "yes," "no," or "maybe" occurring across the research, as shown below:.
Screenshot of Consensus output
Litmaps helps researchers understand the "position” of a paper in the disciplinary conversation by using graph analysis to help researchers envision “maps” of related papers from a single seed article, as shown below:
Screenshot of Litmaps output
Tools like these allow researchers to quickly find and understand a body of literature so they can spend more time deeply reading only the most relevant papers (because less time is required for hunting and pecking to find the sources).
Evaluating Depth Critically
Of course, faster does not equate to better, so humans must stay in the loop, examining all that speedily output literature and evaluating its usefulness. Because AI increases the speed, researchers will quickly find abundant sources (expanding the potential breadth of their literature review); however, humans need to separate the wheat from the chaff. One way to evaluate which sources are most critical to your argument is for researchers to scaffold the synthesis process using tools like Scite and Elicit. These applications can help researchers see how each source contributes to their argument by focusing on synthesizing sources (as opposed to summarizing).
Scite helps researchers by providing “Smart Citations,” which indicate how many studies support or oppose claims in a given paper. This allows the human to engage in deeper analysis of the “conversation” happening in the field, expanding their depth of understanding. Below is a screenshot showing a reference found by the Scite Assistant, with the smart citation tab showing that 77 papers cited the article, there was 1 paper supporting the excerpt, 45 mentioning, and 0 contrasting.
Screenshot of smart citation in Scite.ai
Along those same lines, Elicit and SciSpace amalgamate findings and extracts methods and materials relevant to research questions. Elicit creates a literature matrix with columns aligning aspects of research such as participants, setting, intervention, conclusions, etc. The first screenshot below shows Elicit’s matrix, followed by a view of SciSpace’s version of this same function — both have similar output styles.
Screenshot from Elicit.org’s user interface
Screenshot of SciSpace’s output
Expanding Interdisciplinary Breadth
Breadth is also key for comprehensive reviews. Since AI systems like Research Rabbit can detect intricate connections and allow the discovery of wider arrays of interdisciplinary sources, which a traditional, structured search query might miss. Users can overcome challenges reading academic papers and efficiently stay updated on the latest research without excessive reading time. ResearchRabbit labels itself “Spotify for Papers,” boasting the use of collections that learn your interests and improve recommendations over time. It can also provide personalized digests delivered directly to your email and create interactive visualizations. ResearchRabbit also allows for collaborative research workflows for teams. This tool supports understanding complex texts, expanding the breadth of exploration.
Screenshot of ResearchRabbit interface
How to approach the process responsibly: Keeping a “human in the loop”
It’s very important for us not to abandon traditional search methods for flashy shortcuts. The best way to incorporate tools like the ones mentioned above is to triangulate their use alongside your use of library databases and applications like Google Scholar. The natural language output of AI-based tools is an effective starting point for wrapping your head around a body of literature. However, each has slightly different types of databases that may cater to certain disciplines (e.g., biomedical sciences in the case of Scite.ai). So, to ensure that nothing gets overlooked, it’s crucial to use keywords you learn during interactions with AI-based tools to find other literature — especially if you’re dissertating.
Overall, these tools offer a range of features such as paper discovery, visualization of connections between papers, smart citation analysis, and assistance in automating various research operations. Researchers can choose the tool that best fits their literature review and academic research needs. Rather than viewing AI literature tools as replacements for traditional review methods, the most empowered approach is to selectively incorporate them as augmentations aligned to specific needs within a combined workflow. The table below clarifies the unique value proposition of leading tools to help researchers determine which solutions can responsibly elevate their processes. By understanding optimal use cases, literature reviews can become less cumbersome while upholding the critical human discernment fundamental to this foundational academic skill.
Comparison table of best lit search tools
