Top 7 AI tools to assist researchers and improve research processes

This article describes what a research process is, explaining the 8 main steps in a research journey. It then highlights what the top 3 main pain points are for researchers along this process: time Vs scope of data; tracking sources; and communicating findings. Given this understanding, the top 7 AI-powered tools that can assist researchers are proposed and introduced. These tools cover all phases of the research journey. Special care is given to address the main pain points.

CONTENTS

1. What is a research process?

   • Selecting the research topic

   • Formulating research objectives

   • Conducting a literature review

   • Decide on a data collection strategy

   • Collect data

   • Data analysis

   • Refining research conclusions

   • Completion: compile, export and publish

2. What are the pain points researchers struggle with?

   • Time Vs Scope of data

   • Tracking sources

   • Communicating findings

3. AI Tools that improve efficiency in research processes

   • Grammarly

   • Typeset.io

   • Bit.ai

   • Scrivener

   • Endnote

   • DocInsights

   • Iris.ai

WHAT IS A RESEARCH PROCESS?

There are divergent views on this topic. Some divergences arise depending on the discipline in which one is researching. For example, certain types of ethnographic research may follow a different pathway from that of a chemist. Ethnographic research has more data ‘discovery’ and less emphasis on a hypothesis that must be proven or disproven. Nevertheless, the following breakdown of stages is a broad overview for most research processes, and one can apply them and remain open to customisations. The focus I have taken here is that of a Doctorate candidate, but it is applicable beyond this.

1. Selecting the research topic

This is probably the most underrated stage of the research process. It is certainly the most explorative and touchy-feely phase. However, it is hugely important and should not be rushed! A lot of people start with a very broad idea, an umbrella area in their discipline that they find appealing or has been awarded funding. Some people are given their research topics by the chair of a research fund who has already delegated a series of topics to be covered over the funding cycle. Whilst funding is of utmost importance, you will hate yourself for agreeing to do a topic you are not abnormally obsessed with. I don’t say this lightly. You will live and dream and eat and sleep this topic for 4 - 10 yrs. You better love it. It is also important that the research matters in some way. This will provide you with necessary motivation during the low periods and energy slumps. Really probe into why you are investing so many precious resources in this topic? In what way does contributing to this field/topic make a positive impact? Is it just topical, or is it truly meaningful?

2. Formulating research objectives, questions and/or hypotheses

This is the most creative stage of the process. You really have to get your hands dirty and dig against the grain of the research topic to find probing, worthwhile questions and objectives. If it’s a PhD you’re working towards, you have to verify the originality of your knowledge contribution to your field. This stage often requires many rounds of revisions and a lot of input from the research supervisor. It also requires a lot of reading and discovery into your topic; you need to already be deeply familiar with past papers, published books, journal articles, and explorative new areas of the topic.

3. Conducting a literature review

This stage takes a long time and requires a large amount of data processing. You need to survey a vast range of secondary data sources including academic journals and books, newspapers, magazines, online resources, and so on. This is the portion of the final dissertation in which you indicate to readers and evaluators that you have ‘earned your stripes’ in the field; you know the topic inside out, and you are aware that you humbly proceed on the foundations of giants who conquered before you. 

4. Decide on data collection strategy

Leaning on the advice from your supervisor, you need to critically analyse certain methods of data collection over others. You have to weigh up advantages against disadvantages and then formulate your case specific plan for how you are going to collect data. If you are part of a team of researches this stage requires a lot of collaboration and task organisation.

5. Collect data

Data collection can happen in one round, or numerous rounds, spread over numerous years. It depends on your topic. Over this stage you need to be keeping tabs on findings and ordering your data so it is legible and can reveal results/insights for you in the analysis stage to follow.

6. Data analysis

This is when you evaluate whether you have met your objectives or not. Has the data revealed enough for a dissertation and unique knowledge contribution, or do you need to pivot your focus? At this stage you are narrowing down a vast scope of research and data into some key, critical insights. 

7. Refining conclusions

With the help of your team and supervisor you need to be able to draw conclusions, and if you haven’t yet done so, complete the dissertation or write up. This is also the time that you acknowledge the research limitations and shortfalls.

8. Completion: export, compile and publish

Your dissertation draft goes through a couple of rounds of editing and refinement. This stage can take way longer than you anticipate, for some because the bibliography is so huge and unwieldy! Once your supervisor and team signs off on the ‘final version’, you can prepare the document for submission to be reviewed and moderated (or, published).

WHAT ARE THE PAIN POINTS RESEARCHERS ENCOUNTER?

There are three pain points: time vs scope of data; tracking sources; communicating findings/concepts.

1. Time Vs Scope of data

There is a tension between the sheer volume of literature and data that needs to be managed and the practicality of time = money. The more deeply and widely you can analyse your field, the better questions and research objectives you can come up with. However, research is often limited to tiny aspects and sections of a field for the practical reason that one is not able to digest a larger scope of literature in the time frame research funding covers. Moreover, there are times when research is published with ‘blindspots’ that are later picked up on. These are not always a result of time Vs scope of data (of course, human error and other factors are at play) but can be.

2. Tracking sources

This, again, is a pain point that arises from the sheer volume of data that needs to be processed. How do you organise your sources and backlinks? How do you keep information that was seen early on in the research phase that will be relevant to you later on? How do you remind yourself it is relevant to you at the right time? This is particularly difficult when working in a research team because everyone’s sources are relevant to everyone else, and there is a lot of cross-referencing and collaboration in writing that needs to happen. Collating a bibliography is often a nightmare experience for researchers!

3. Communicating findings

Communicating what you’ve learnt and discovered, and drawing conclusions from your research, is absolutely essential. Being a researcher means that you enter into a conversion with others in your field. But how to best articulate findings that are often not in words or graphs, but in abstract numbers, or other varying shades of grey? This can take ages to get right, and sometimes really profound findings are initially not recognised because they are not adequately put into context, presented, and argued.

AI TOOLS THAT IMPROVE EFFICIENCY IN RESEARCH PROCESSES

Grammarly

Grammarly is the leading AI-powered writing assistant. It has a free extension on your browser and gives grammar, spelling, tone and structure suggestions across email, document software, project organiser software, and social media platforms. For researchers, this AI tool serves as a fairly comprehensive copyeditor - on hand for free - as you are writing up your work. The downside of Grammarly is that it is not specifically for researchers, and certain research nuances may fall through the cracks.

Typeset

Typeset is the more powerful, researcher-specific version of MS-Word or Pages. They boast over 100,000+ verified research journal templates to choose from, and they claim their software will increase your chances of getting published! In addition, they offer they own native version of Grammarly, attending to all your grammar, spelling, syntax and semantic needs. A very cool, researcher-specific AI function of Typeset is the plagiarism checker. They also enable you to collaborate with teams on the document. Lastly, they have included a publishing stream from their platform directly to journals.

Bit.ai

Dubbed the ‘world’s most powerful workplace and document collaboration platform’, Bit.ai is used and trusted by professionals in many disciplines. It is highly scalable, and thus suited to larger research projects that require multiple stakeholders and contribution streams. They call their platform a space for ‘fully integrated smart living documents’. And certainly, there are wonderful options for smart infographics and graphs, editing tools, collaboration trackers, citation and source finding tools, smart widgets, public cloud integrations, theme designs and even a smart tool for interlinking documents. Bit.ai would be most useful to a research team or department that publishes not just in journals, but across publications and the web.

Scrivener

Scrivener is the trendy go-to app for writers of all tribes. It looks and feels like it was developed by creatives and writers for creatives and writers, with sweet and smooth features to help you order your creative process. It is particularly useful for those who make a lot of notes. Notes about notes over years - how do you order and remember to recollect those notes in the future when you need them? Scrivener is addressing just this problem. They also try to assist the flow of creativity through their interface and improve your writing style through natural language processing. The feature for researchers to place source material on the screen alongside the document page been worked on is particularly useful. Lastly, Scrivener offers templates and smooth publication processing to help you self-publish and take your work to market. It is probably more suited to Humanities and Social Sciences than other research disciplines that rely on a lot of quantitative analysis and presentation.

Endnote

The question Endnote proposes on their marketing website is: “Did you know that researchers waste nearly 200,000 hours per year formatting citations?” Their AI-powered citation tool addresses just this problem of wasted time. Endnote offers you a way to accelerate your research process not just in terms of the final compilation and citation checking, but also in terms of a tool to lock-in source links as you write. It offers integrations with MS-Word and allows team collaboration access and sharing. It is also useful for the digital-nomad researcher, as all the citations are keep on a cloud, accessible wherever you are. 

DocInsights

DocInsights is a proudly African AI tool for researchers, legals, auditors, insurers and any other professional who deals with a large volume of data. DocInsights not only offers proprietary AI smart search functionality to laser beam through any level of document and data complexity, but it also has seamless integration with custom and public cloud platforms. So the journey from data source to findings is a smooth one. Moreover, once you have generated findings from the AI search, you can annotate and collaborate across your team using Adobe annotation tools, which are fully integrated with the DocInsights software. They offer three more key functions - a smart chronology builder to arrange your findings according to a chronology logic of your choosing, auto back-linking to help you build a bibliography and keep track of sources, and you can export your findings in a presentable, workable PDF.

Iris.ai

Iris.ai is an award-winning AI engine for scientific text comprehension, searching, extraction and surveillance. It is the go-to for science-based researchers today. Think Google Scholar (could also be featured in this article, but didn’t quite pass muster given the prevalence of Iris.ai) but for the sciences that is customisable and a lot more powerful. Iris.ai proclaim that their ultimate goal is to build an AI researcher that will be essential to any research and development team across disciplines. Iris.ai natural language understanding tools can comprehend the context of technical, medical and scientific language, unearthing matches and insights that are not obvious to the eye, and certainly beyond the reach of conventional search engine capacity.

For more information about how DocInsights can assist your research process, get in touch with the Doc Insights team and book a demo. Doc Insights offers subscription solutions and custom enterprise solutions.