Academic research is a complex and arduous endeavour, even more so during a graduate program. The research process is meandering and often messy, requiring considerable time and effort.
After narrowing down a research topic or question, you have to first conduct a literature review to gather all the information that has already been published in the field. This is a crucial stage of the research process because it helps you develop a hypothesis to guide your own value-adding research. Moreover, the only way to make genuine contributions to your discipline is to first understand and acknowledge what other scholars have already published on the topic.
Once you begin a literature review, reading one research paper leads you to five others, each of which might lead you to several more scholarly works through citations. Abstracts often don’t provide enough information for you to decide whether those papers are relevant to your review. Before you know it, you’re already overwhelmed by the vast number of papers to sort through, read, and summarise. You’re also probably wasting precious time reading papers that are off-topic or only tangentially connected to your topic.
This is where artificial intelligence (AI) comes into play. AI can support your graduate-level research by reducing the time you spend on literature reviews, while still enabling you to produce high-quality of research.
Common research challenges for students
While producing original research at the graduate level can be rewarding, it is also challenging: every claim you make needs to be backed by data and rigorous analysis. Here are the significant challenges graduate students face while conducting research for their graduate theses:
It’s no surprise that graduate students have chock-a-block calendars. As a graduate student, you’re probably enrolled in multiple courses with varying deadlines and have work commitments as a research assistant (RA) or teaching assistant (TA), leaving you little time to focus on your own research.
In an ideal world, you’d love to peruse all the research papers related to your topic of interest, but your limited time makes this impossible. Given your time constraints, it’s essential for you to prioritise papers that add significant value to your research.
A sharp rise in research publications
Academic research has witnessed massive growth globally. According to Emerald Open Research, the number of academic publications worldwide rose from 0.65 million in 1980 to 3.16 million in 2018, an increase no doubt fuelled by the increasing pressure on academics to publish research in order to stay relevant. Given the sheer amount of research published every year, it’s difficult to even fathom where to begin, let alone muster up the energy for an exhaustive literature review.
Increase in the number of working papers
When teaching-focused institutions and academics feel under pressure to publish research, the results are often mediocre. Given this pressure, ‘predatory journals’ are also on the rise.
In addition, an increasing number of working papers or preprints are making research available before it’s peer-reviewed or published in a journal. As a graduate student conducting a literature review, you have to spend more time and effort verifying if the information in these papers is reliable and supported by robust data, adding complexity to the process.
Complexity of academic publications
Depending on your discipline and the stage of your educational journey, it can be difficult to decipher and understand some research papers. For example, technical disciplines such as Mathematics and Economics are fairly linear and build on prerequisite knowledge. If you’re in the early years of your PhD program, you may have yet to develop the capability to understand more advanced research, which can substantially increase your time spent on such papers.
How can AI help?
The use of AI in research mitigates most, if not all, of these challenges normally faced by graduate students. Some AI tools can break down and summarise large amounts of complex research very quickly. This software helps you to screen and synthesise multiple papers in a fraction of the time it would take to skim-read them, making it especially useful for graduate research.
AI tools can operate on “potentially fuzzy, weakly structured, and unstructured data” that exists as bibliographical meta-data or full-text documents. Natural language processing (NLP) considers not just the syntactic meaning but also the semantic meaning, which significantly improves searching and screening tasks.
In addition, advanced machine learning (ML) techniques like deep learning, can be trained to emulate the decisions of researchers to elucidate and systematise multiple rules. ML techniques can even automate decisions when exact rules are hard to specify.
Scholarcy is an AI-powered article summarise that takes over the mechanical and banal aspects of literature reviews, allowing you to focus instead on the critical analysis. Scholarcy’stechnology understands how academic papers are written. It can highlight the important phrases and contributions made by the authors, so you spend less time screening the paper for significant statements. Scholarcy can also create a referenced summary that you can customise by choosing the number of words, level of highlighting and language variation. While Scholarcy is primarily for academic papers, you can also use it for white papers, company documents, and other types of reports.