Artificial Intelligence And The Academic World

The academic ecosystem has not been exempted from the revolutionary effects of Artificial Intelligence [AI] & Machine Learning [ML]. 

As one would expect, a world that relies heavily on metrics, calculation, prediction & theories will be a fertile ground for AI & ML technologies. 

The world of research, in general, is not new to software & computing. 

Major tools used in research are software that runs on programs designed to assist researchers and scholars with the very basic aspects of research activities.

There is so much automation & repetition in research, so research software can easily handle most of the automation & monotonous numerical works associated with research. 

But AI is unique, it works on different & more complex computational principles. 

Software can’t do certain things without the direct external intervention of a human element [software is totally dependent on human input]. On the other hand, AI algorithms can make smart decisions based on the type of data available, with minimal intervention from a human element. 

These smart algorithms learn based on the nature & quality of data given to them. 

State-of-the-art AI tools are increasingly being used in academic research, making research easy & enjoyable for scholars. 

And researchers are always on the lookout for methods that can enhance the research process [ease their writing & publishing efforts & make data analysis more efficient] while ensuring that the quality of work produced is preserved. 

“Scholarly output across the world has tripled in the last two decades.”

Enago Academy

Few scholars still fully understand the importance of AI in research, and this is a major hindrance to the full adoption of these technologies in the world of academic research, nevertheless, there has been a significant improvement in the adoption of AI technologies by academics, scholars & researchers alike over the past decades.

How Important Is AI To The Academic World?

It’s no surprise that researchers who are Millenials [1981 – 1996] and “Generation Z [Gen Z]” [born from 1997 – 2012] the digital generation are the population conversant with the technicalities & complexities of the digital era. 

Though there are older researchers who are already adapting to the use of AI in research, however, the uptake of the use of AI in research is popular among Millenials & Generation Z as compared to the older generation of researchers & scholars. 

The younger generation of scholars [Millenials & Generation Z] makes up the larger percentage of researchers worldwide. 

They have grown up with the internet & are used to technology. And the pandemic, which was a major influence on the uptake of digital technology solutions in various industries contributes significantly to the adoption of AI [and other digital technologies] in research and other aspects of human life. 

AI can be used for improving language, enhancing text, reducing cost, and making research writing fast.

AI-based solutions will give researchers more time & energy to attend to other high-impact activities, like thinking & developing fresh ideas, connecting & networking with other researchers, and coordinating logistics that come with conducting research, amongst others. While mundane [but critical] tasks like editing, proofreading, data sorting & analytics are reserved for AI algorithms. 

There are several AI-based tools that researchers are already utilizing in the field of academic research. 

These include; 

• Trinka AI [grammar checker desiged for academic writing]
• Elsevier Journal Finder [shortlist journals that are highly suitable for your work] 
• Grammarly [improve grammar & writing] Mendeley 
• Elink.io 
• AuthorOne [ensure your manuscript is submission-ready] 
• CLARA 
• GantPRO 
• Bit.ai 
• Typeset.io 
• Wisdom.ai 
• ProwiritingAid 
• Scrivener
• Turnitin
• DeepL

And a host of other smart AI-based academic research solutions. 

Here also is a list of some of the major applications of AI in academic publishing & research. 

• Plagiarism Detection 
• Image Recognition
• Data Analytics
• Language Enhancement
• Text Analysis
• Text Summarization 
• Grammar Checks
• Metadata Creation Identification 
• Content Extraction & Creation
• Translation 
• Copyright Checks
• Content Discovery

Novel applications of AI to academic research, like “bots” that write manuscripts [although this may sound too good to be true], automated reasoning & logic are not yet common among researchers. 

There are research activities that are outrightly impossible for researchers to perform manually today due to the colossal amount of data available on the internet — Like plagiarism checks. It can take a whole lot of time to do this manually.

This is where AI comes into play. 

With very minimal supervision from human elements, AI will make the research process easy for researchers. 

But this does not come without some concerns or challenges. 

Challenges, Concerns & Obstacles

Who said these “smart” tools are perfect?

We must understand the challenges of incorporating such advanced technologies into our daily lives. 

Are these tools reliable? This is one question researchers must ask themselves. Though a couple of the popular tools have proven over time that we can rely on them, what about the newer [and more advanced & complex] ones that are being introduced into the academic research market? 

What about accuracy and consistency? 

Can we rely on them to help eliminate as much bias as possible? Like bots that can help you write your manuscript or algorithms trained to perform deep logical reasoning. 

What about the replicability of generated output? And also the nature of data generated being user friendly & scalability. 

Researchers rely heavily on data. Without it, there is no research. The quality of data [data fitness] produced by these algorithms must be of high quality — it must be accurate, it must align with real-world conditions. 

Inaccurate data can lead to incorrect conclusions and hence flawed decisions. Users have trust & confidence in data that is of high quality, so, whatever data AI-based tools generate must be suitable. 

More Challenges

As explained earlier, AI-based technologies feed on data. These algorithms are only as smart or as powerful as the quality or nature of data available. 

In a field where we don’t have enough quality data, then we are seriously limited as to how we can use AI-based tools. 

Another challenge that we must take note of is in the nuances of human language that machines may not understand — too many interpretations, perspectives, and idiosyncrasies. 

Will AI be able to solve certain deep-seated problems in the academic world? 

Like detecting fake data, recognizing predatory publications, and challenges with review bias & inaccurate translations. 

These are some considerations that we must place under the spotlight when choosing AI-based academic research tools. 

However, developers in the world of AI technologies are consistently working to make sure that these perceived and actual gaps are closed. 

In the future, we will be seeing more sophisticated algorithms that will increase our confidence in Artificial Intelligence. 

Primary Obstacles To The Implementation of AI According To Recent Global Survey Report

According to a recent Global report/survey by Enago, here are the major obstacles to the implementation of AI technologies in academic research: 

• Lack of competencies/understanding of AI
• Difficulties in integrating AI-based solutions with existing infrastructure
• Requires dependence on external expertise for acquiring AI skills
• Lack of technical infrastructure/requirement of machines with high computational speed
• Scarcity of specialists [trained AI staff]/greater dependence on an external skilled workforce
• Organization/company does not yet recognize the need for AI
• Cost of implementation/financial investments
• Cost of implementation too high [large upfront costs associated with researching and implementing AI solutions]
• Lack of standards
• Uncertain ROI
• Legal concerns, risk, or compliance issues

[Source: Enago]

The Future of AI In Research 

The Age of Artificial Intelligence

In the nearest future, it’s a reality that most of the jobs that we see today in the world of academic research [and in other fields/industries] will no longer be available. 

We will have super-smart AI algorithms that will become so smart to the extent that certain human jobs will no longer be occupied by real people. 

Take, for instance, the transportation industry, where in the future cars drive themselves. Will there be any need for real drivers? Not certain, but it’s going to become a reality. 

The same thing goes for the world of research. Soon, we will have AI algorithms that can do more than 50% of all the research activities, leaving the more social, emotional & interactive duties to human beings. 

Some experts in AI are deeply concerned with the way AI is going, predicting that a time can come when AI can become independent and can think for itself. Ok, that sounds like something out of a Marvel movie, but it’s likely to happen with the rate at which we are going. 

AI can be likened to a two-edged sword. While having potential benefits it also has challenges. 

How Can Researchers & Academics Position Themselves For The Future?

Learn. Keep learning. This is the ultimate advice researchers & academics who want to stand tall in the future of academics must keep in their hearts.

The academic world is always expanding, to include novel ideas & concepts. The scholar must evolve with it so that they will not be left behind. 

In your field, make sure you are always working, looking for ways of contributing to the body of knowledge that already exists. 

The best time to start getting acquainted with AI technologies is now. By the time the future comes, you will become a key player that can never be replaced by an algorithm. 

As an academic passionate about the field of research, there are emerging skills you must develop to stand out in the future of research. And a good number of these skills are in the area of Artificial Intelligence & Machine Learning.

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