It’s true, everyone has felt some kind of dread when faced with tackling a literature review. You are faced with a seemingly endless mountain of research and trying to find “that paper” that connects all of your ideas, while at the same time trying to find it without losing weeks to irrelevant studies. The task can feel like searching for a single, specific needle within a very large haystack; a haystack that continues to grow larger. Sometimes, even the needle may not exist. This is precisely when a modern academic-paper-search-engine not only becomes a valuable tool, it actually feels like an effort to save your sanity and help you meet a deadline.
The Overwhelming Ocean of Information
Do you recall back in the day when you would access a standard database, put some key terms into the search field and receive back thousands of results based on either date or citation count? If you found a relevant article within the first ten results, it was very lucky! The next hundred articles might have had some loose connection to your key terms, and then the rest of the articles would be a complete jumble of irrelevant papers that happened to have mentioned your key term once in their references. Finding the relevant articles from a huge dataset like that was the most difficult part! You’d spend most of your time reading through hundreds of abstracts just to find that either you needed to pay to access the article in full or that the content of the article had nothing to do with your information need. The entire experience was traditional, manual and very, very slow. At that time, the academic search engine for papers was a directory and not a guide. The bulk of the intellectual effort in creating new knowledge (connecting the dots between different areas of study and synthesizing new knowledge) has been offset by the work that was done to find the dots to begin with.
Now, let’s unlock an intelligent academic paper research engine, which applies artificial intelligence technology to get more than just the title and title-reference search results, but understands what documents are about. Imagine that, for example, you were to ask this academic paper finding engine “What has been the current issues with quantum computing error correction?” The intelligent academic paper research engine would be able to look through multiple millions of academic papers and return to you only the most relevant documents that contain true answers, whereas traditional search engines would return documents that contain only keywords such as quantum, computing and error, without truly answering your question. The result of using the intelligent academic paper research engine to conduct search queries is one where you engage in dialogue with your research assistant, not just search through a database like with a traditional search engine. The engine learns from you. And provides you only with documents that are relevant to your specific research questions on an ongoing basis.
Beyond Keywords: The Semantic Search Revolution
The secret to why traditional searches don’t work well lies in that traditional search engines rely on simply matching words. However, the language used in academia tends to make it difficult for search engines by using a lot of complex phrases for similar ideas, as one word (neural network) may be found in multiple different disciplines or academic fields but have various descriptions to encompass the same idea (i.e. “deep learning” is also sometimes referred to as “neural network”). If searching using just the phrase “machine learning in ecology” you would miss out on reading an important publication that utilized the phrase “computing species distribution models.” An intelligent academic search engine uses natural language processing (NLP) technology to enable it to understand the semantics of academic papers and therefore to understand that “neural networks”, and “deep learning” are related even without there being any actual shared words between the two phrases. Additionally, it does this by creating a map of similar concepts within a large multi-dimensional space.
The ability to conduct interdisciplinary research has become essential for producing innovative solutions in today’s world, and much of that research is being conducted by multiple disciplines working together. For example, if a molecular biologist were conducting research on using biomimetic techniques to build better robotics, they may want other perspectives from other disciplines, such as materials science, engineering, and zoology, that could help support their research. Searching for information by manually cross-checking these numerous different disciplines, all of which have their own separate databases, is tedious, to say the least. Nevertheless, utilizing a sophisticated adaptations of academic search engines can allow researchers to quickly search for information that may be useful to them, regardless of whether the search term(s) used match the subject of the article(s) being reviewed. Thus, the ability for researchers to evaluate research resources from a variety of different subject areas will allow them to incorporate a wider range of interdisciplinary research into their own work through use of AI-assistance.
The Time-Saving Alchemy of Smart Filters and Recommendations
Let me discuss time, a resource that cannot be replaced or reproduced. A literature review has taken multiple months to complete. An academic paper finder can greatly reduce this amount of time. This will occur by providing intelligent filtering and by providing proactive recommendations. Instead of simply having filters that allow you to filter results based on the date of publication or journal title, the best academic paper finder will provide filters that allow you to filter by concepts, methodology, and degree of connection to your “seed” papers. You can also request that you only see papers that have opposing viewpoints to the papers that you are currently reviewing, thereby eliminating the possibility of experiencing confirmation bias.
An even stronger feature is the recommendations engine. Once you’ve identified one or two articles as highly relevant to your research topic, the AI doesn’t merely pull other papers based on similarities on a goodwill basis; it uses these articles to identify other papers which either (1) have cited one of those highly relevant articles or (2) have been cited by those highly relevant articles. In essence, you get to see the growth of the body of knowledge surrounding a specific topic – through both theoretical and practical perspectives. The recommendations come not only from what you already know but will likely provide a deeper and broader foundation to your work; giving your review greater depth and scope. The hours you’ve saved are not solely from expediting the time spent looking for the necessary articles; you have also expedited the length of time it takes you to arrive at your conclusions because you’ve removed the burdensome nature of having to search for articles in an on-going basis.
Taming the Citation Beast
After you’ve completed the initial data-collection process and have found your 80 valuable academic resources, you’re left with the nightmare task of organizing all of these resources. Each PDF needs to be properly managed, each citation extracted, a set of notes taken for each resource, and you’ll need to somehow retain the memory of why you saved each resource in the first place! An integrated academic resource search and management system can save you a great deal of time and frustration through an integrated research; several of today’s academic resource search platforms have evolved into full research management systems. They allow you to add resources into a cloud storage library with one click, automatically create correctly formatted citations for any style required, and completely link your notes directly back to the PDF that you’re using for reference.
Several tools can quickly summarize the primary components of each research study so that you can determine its relevance prior to reading the entire study, thereby aiding in the prioritization/triage of your reading list. In addition, through machine learning, the AI can analyze your article library to find connections to other articles saved in your library and visually depict these relationships among different research articles. This is a very helpful resource for creating the literature review for your research. Rather than glossing through your digital library and trying to derive some order from an unorganized stack of articles, you may see clusters of common themes develop, identify the primary areas of debate and discern the areas where there is lack of research; all of these elements represent the foundation of a strong literature review chapter. Therefore, academic paper search engine change from simply locating research material to being a partner in both organizing and synthesizing your research material.
Democratizing Access and Serendipity
The democratizing effect of AI is significant as many researchers do not have access to the many expensive databases of larger academic institutions. Paywalls still create a significant hurdle between researchers and scientific materials. There are many AI-based search engines that combine open-access repositories with pre-print servers and institutional archives, allowing for more researchers to search for and find knowledge freely. This helps provide equality for independent scholars, smaller institution researchers, and researchers in developing countries.
In addition, these engines reintroduce an element of chance (the finding of something which you did not know you were seeking) in a rational manner. That is, due to the fact that the AI understands the content of the papers it will make recommendations which are sometimes unexpected and surprising, but have relevance to what you have asked for; for instance, if you are searching for papers regarding `XXXX`, then maybe the AI will recommend you review a group of papers representing a different discipline than you would normally look at, therefore giving you a completely different perspective and new avenues for investigation into your own review. This controlled chance will produce a greater yield than the random process of browsing the shelves of a library; therefore, the opportunity to explore is being guided and expanding your horizons in a direction that is directly related to your review.
Yes, an AI academic paper search engine is a game-changer for literature reviews. The difference between being thrown into an ocean with a net and being given a submarine with sonar and a detailed map. An AI academic paper search engine solves the overwhelming amount of information available, interprets the jargon of academia, saves you a lot of time, organizes your chaos, and makes the process of doing research more accessible and creatively productive. It won’t do your thinking for you (that is still your incredible, human job) but it eliminates every obstacle possible between you and the information you need to do your thinking. For anybody who has faced the daunting task of completing a literature review, that is not just a benefit, but a revolution.
