Let’s face it – if you’ve ever had to search through academic papers you can relate – digging through academic literature is like getting an atlas with no key and written in a language you don’t know, with a stack of papers being added to it. You go from one tab to another looking for interrelated citations until your eyes can’t take any more, but you always still wonder if the paper that answers your question is the one you haven’t seen. Wispaper is not the quiet whisper of an academic search engine that has you think of searching the academic market differently, as they are more of an academic research partner that completely understands you. This is not to say that wispaper has pretty images or that it is a little faster than any other academic database; they consider rethinking how we find literature and research for academic purposes within academia at 100%. Wispaper is using AI to change how scholars search for literature and how literature reviews are done by clearly defining that researchers are not looking for websites; they are looking for interrelations, context and clarity from a sea of conflicting PDFs.
The AI-Powered Search: Beyond Keywords
Typically, searching for scholarly literature is like searching a dictionary in an effort to find the definition of a particular word or words. To find only scholarly articles, most people simply guess at what they believe are appropriate keywords. After attempting multiple searches with various combinations of keywords and finding results that do not match their original search, they continue the process in the hope that eventually they will find an article that fits their needs. Wispaper changes this approach by allowing users to submit their ideas, or questions in English and by providing users with relevant scholarly articles that are based on both the user’s intended meaning and the context of the user’s question. For example, when asking for an article regarding “machine learning in climate models,” Wispaper will also provide users with specific methodologies, interdisciplinary applications, and foundational theories regarding machine learning in climate models based upon what Wispaper knows about how people will be using those articles.
The intelligence provided by these systems comes from significant volumes of text from academic document sources being processed using neural networks as well as other models that use deep learning (with many documents containing similar terminology being included into one document category, termed “Digital Files”). However, these types of systems do not “know” how to relate these concepts – they only process them. When you do a search for “neural networks applied to genomics” in Wispaper, for example, it will provide you with documents related to both of those topics with high confidence because the underlying AI system recognizes that there is some conceptual relationship between the two documents, even though they were written years apart from each other. In this way, the AI eliminates a lot of irrelevant data from the search results, therefore allowing you to generate a list of curated searches that have a much greater likelihood of being viable, informative and useful to you. In addition to filtering the search, the AI also provides guidance for other related searches that you may not have previously thought of doing.
Also, you can customize your search through wispaper’s interactive way of searching. As you go through the results, the system will track what parts of the abstracts you view, save or cite, and adjust future recommendations according to this information. This creates a feedback loop of continual improvement in how well the wispaper understands your personal search style and research focus. For researchers, this changes the relationship between searching for literature and finding relevant literature from simply repeating a previous search to developing a dialogue with the literature you are reading, where every time you perform a search, each new result leads to new areas of investigation within your discipline.
Wispaper’s Literature Review Revolution: From Reading to Synthesizing
After you’ve obtained a mountain of potentially useful research articles, it’s time to conduct the actual literature review. This task can often be where many projects come to a halt, buried in a mountain of PDF documents and handwritten notes. However, Wispaper is attempting to solve that issue head-on by introducing artificial intelligence tools that are intended not only to help you organize but also to assist in the synthesis of related information…into something useful! One of their most powerful tools is automatic summarization. However, this is not like your basic highlight screen summary; rather, it provides a short, structured abstract of each paper being reviewed and will often contain the author’s major contributions, methodology, and the findings of that paper, all in relation to the term being explored, or other saved searched term topics. This will allow you access to everything contained within the paper without losing many of the important details – allowing you to quickly assess whether the paper needs to be read further.
However, Wispaper can do so much more! For example, if you upload an entire set of 50 papers for a literature review chapter, the AI will process the whole set and identify common threads in the papers, identify the major controversies or debates within the literature, suggest gaps in the research area, and show how the papers intellectually relate to each other. The AI can create visual maps showing how the ideas in each paper connect and how they have evolved through time. This representation of the literature is groundbreaking. Rather than spending weeks either taking manual notes or trying to sense patterns through your own efforts, you can now receive an AI assisted, overview of the landscape. You will be able to quickly identify which authors are communicating with each other, which methodologies are the most utilized among the authors, and if there are any unexplored gaps.
Wispaper will also assist users with the writing process by generating outlines for literature reviews and suggesting headings for the various sections based on the themes synthesized. Wispaper can help you with actual writing by drafting explanatory paragraphs based on what has been found in previous studies (i.e., what the consensus is the disagreement was) and act as a co-author with an eidetic memory of every paper that you have inputted into the system. In addition, it provides citation integrity, so users can return to the original sources in their reference list to verify claims within their work. While this does not replace critical reading of literature, it facilitates the user’s ability to conduct higher-order thinking, critique others’ work, and create original arguments. Wispaper does the majority of the organisation and synthesising for the user, so that they have mental space to conduct higher-order thinking.
The Wisdom of Networks: Connecting the Dots
Academic knowledge is not self-contained. Different academic papers reference one another, either building off of, disputing or expanding on previous work. Wispaper creates new ways to visualize and navigate these reference networks through advanced uses of artificial intelligence (AI). One such way is through citation analysis, which allows Wispaper to tell not only how many times a given paper has been cited, but also provide analysis regarding how each paper is connected based on the purposes for which it has been cited (i.e., as a foundational method versus as a disputable counterpoint; as a bridge between previously unrelated subfields, etc.). Wispaper uses this relational mapping between papers to show you how a particular paper functions in relation to its academic ecosystem.
Map author and institute relationships using network intelligence. With it, you can find out who is publishing about a given topic, and how they are connected through collaboration, mentorship, and debate. You can also see which researchers are part of an emerging research cluster, which lesser-known researchers are considered influential, and what topics you might expect to see trending through the growth and connection of citations over time. This knowledge provides great value to scholars; it situates your scholarship within the greater dialogue of your area of study, identifies potential collaborators and reviewers, and informs your understanding of both the social structure and the intellectual structure of your scholarly area.
Moreover, wispaper utilizes these systems to fuel the experience of serendipitous discovery via the AI that provides you with suggestions for finding “conceptually adjacent” research based on your past research; research that you may have never located using conventional means due to different vocabulary or publications within otherwise unfamiliar journals but presents similarly related problems or alternate views of the same problem. Cross-pollination provides much opportunity for new ideas to be born. By continually mapping and travelling the implicit pathways of the scientific community, wispaper has transformed academic literature from an unchanging repository into a real-time, discoverable network of information.
A Tool for Every Stage: The Integrated Workflow
Wispaper combines all these AI features into a single cohesive workflow and is thus able to provide an effective way for researchers to manage their research. In essence wispaper gives researchers everything they need to complete their research in one user-friendly environment. Wispaper begins with a smart conversational interface that allows researchers to conduct searches that are conversational in nature. Once research papers are found, they can be saved in personal AI-controlled libraries which can automatically tag, categorise and classify paper content, removing the need for time-consuming manual organisation. When researchers read papers using the wispaper interface or upload their annotated PDF files into the system, the system will extract all of their notes and highlight back to the original document and other papers that are pertinent to their document within the researcher’s library.
When it’s time to create your written text, you will find all of this compiled information available to you with the click of a button (or several). Your bibliographies can now be created for you and you can use this application to ensure that all of your citations have been in a consistent format. You can also use this application to help find any gaps that exist within your literature that has been reviewed to date. The more efficiently that you use Wispaper, the better it will understand your project and, therefore, the better assistance it will provide for you. This integration makes it easy to move between reference managers processing programs taking programs since it is all located within one intelligent ecosystem based on your research goals.
Adopting a holistic approach will help to develop better scholarly habits. Wispaper, by encouraging researchers to be more engaged with a broader spectrum of literature, helps to demystify the literature review process and assists in creating a richer understanding of scholarly engagement as a collaborative and interrelated experience. Wispaper, by using visualization techniques, helps to create a connection to the larger scholarship network as a whole. Additionally, through assistance with administrative work, wispaper frees researchers up to concentrate their energy on what is most important: engaging in critical thought, analysis and creation of new knowledge. Ultimately, wisaper’s goal is to be the operating system for modern research, where AI will handle the overall complexity in the background, empowering human curiosity and intellect to be the driving force behind progress.
The Human in the Loop: Augmentation, Not Replacement
Wispaper’s philosophy is based on the idea that its AI is meant to be used as an augmentation of what you’re already doing and that it’s not an autonomous agent. It does not perform your thinking for you. Instead, it provides you with insights, organization, and links to help you become more efficient in your thinking process. Summaries are a starting point for you to evaluate critically, and network maps are a starting point for you to explore. Tools for synthesis are there to support the construction of your argument. The platform was created with transparency so that the user can understand how each paper was referenced or connected. This design adds a “human-in-the-loop” aspect to the process so that the researcher is always in control while the AI can be used as a very capable co-pilot to help guide you through the enormous amount of scholarly literature available.
Wispaper is a resource that provides a balanced approach to achieving academic integrity and quality through research integrity and academic rigor, respectively. Wispape demonstrates the ability to enable the discovery and efficiency of digital solutions to be achieved, while not interrupting or bypassing the foundational process of critical reading and scholarly evaluations. Wispaper recognizes that AI can transform how scholars perform research; however, this transformation will not include automation of scholars (removing them from the process), but rather by providing them with “superpowers” – the abilities to conduct research with comprehension, review meta-analysis, and observe conceptual associations within the body of knowledge represented by the research literature. Ultimately, Wispaper reflects that future accelerated research is not based upon AI, but on augmented scholarship that is being developed intelligently.