Artificial Intelligence in Academia

The emergence of generative artificial intelligence (AI) platforms, such as ChatGPT, is already having major repercussions on society and will certainly have many more in the future. Experts are claiming that generative AI will be able to do a plethora of tasks, including blog writing, instruction manual writing, computer coding, marketing and sales, R&D, and even poetry. Generative AI’s influence is sending shockwaves across multiple sectors of the economy and it looks poised to make an even bigger impact in academia. How well university administrators, faculty, and students get ahead of the trend will be important as it will impact the quality of education students receive in the future.

Today, AI generally refers to software or machines that can learn and be utilized for a number of everyday applications such as search engines, virtual assistant, language translation, facial recognition, and more. Without getting into the complex details and technological nuances, generative AI is a more advanced form of AI that is capable of writing texts based on prompts. There are many exciting possibilities for the use of generative AI in academia that will directly affect administrators, faculty, and students by determining eliminating some things but also creating new opportunities in other spaces. So let’s take a look at how AI made its way into academia; some of the benefits and drawbacks it offers administrators, faculty, and students; and some potential future trends.

AI in Education

AI has been around on the periphery of the education sector for many years, with programs such as Grammarly giving students and professors an advantage in their work; but AI has many more uses beyond simply helping one write a better paper. Despite its potential ability to be used to help students take shortcuts, many university administrators, professors, and researchers have embraced AI as a useful tool, so much so that it has even been given its own term and abbreviation, AI in education (AIEd).

Many professors have come to view AI as a beneficial teaching aide that can make their jobs easier but not detract from the quality of their students’ educations. For example, AI can be used to create different summaries of a book, giving students a diverse range of viewpoints and scenarios to consider. Along those same lines, AI can also help professors identify the most effective teaching methods for specific classes (traditional lecture, small group discussion, laboratory, etc.) and can also automate more repetitive tasks such as grading. These and more features of AI can be beneficial to professors and their students, as it gives teachers time to focus on tackling more difficult concepts in class, which will challenge the students and ultimately make them better students in the long term. AI will also give professors more time to dedicate to research and other endeavors that will raise their professional profile and the profile of their universities.

AI is also being utilized in the academic publishing space in a number of roles with different levels of success. AI has been used as a judge and detector of manuscripts that are worthy of being published by a particular journal or publisher, and generative AI has proven to be a much more accurate plagiarism detector than standard programs. 

For many researchers in the hard sciences, writing reports and articles is the most laborious and tedious part of their work, which can create barriers to advancing in the field. Tech-orientated but writing adverse researchers can utilize generative AI platforms such as automatic writing evaluation (AWE) to see if their written results are on par with those of their colleagues and they can also use it to asses their students’ papers. AI can also be used to write abstracts of articles and speed up the overall writing process, but it’s not yet a miracle technology for academics who need help with their writing skills. The AI still has to be fed the information, and as will be seen later, it is so far only limited to basic or low-level texts. 

Students and Generative AI

As much as AIEd will benefit researchers and professors in their work, the extent to which generative AI will be adopted in academia may be determined by students. After all, students are the consumers in the education space and the extent to which they utilize different aspects of AI will play a major role in its adoption. Student use of generative AI will go far beyond having the technology write papers, but will expose students to new, effective ways of learning.

One way that generative AI will benefit students is through the idea known as “contextualized learning.” Although this may sound like a concept that was drawn up in an academic ivory tower, it is a relatively simple application of AI. With contextualized learning, AI platforms will tailor learning plans and lessons to each student’s needs. The AI will be able to identify learning gaps and problem areas of the students, allowing the professors to act accordingly. 

AI can also help students spend their time away from class more productively. Intelligent tutoring systems (ITS) is an AI tutoring platform that helps students prepare for exams, writing assignments, and other assessments by focusing on their strengths and mitigating their weaknesses. As platforms such as ITS develop and progress in the coming years, more students will discover their utility, possibly leading to better grades and test scores in the classroom. 

The Drawbacks of AI in Academia

There’s no doubt that AI offers a plethora of benefits for administrators, faculty, and students in academia, but this will not come without some growing pains. One of the major obstacles facing AIEd is that it’s so new and therefore dependent upon how widely consumers (students), universities, and other institutions accept it. It’s important to know that the major force currently driving AI in academia isn’t necessarily the students, administrators, or faculty, but the private companies creating the AIEd platforms. Currently, there is a bottleneck between the producers of AIEd and the consumers, with private companies noting that there are currently too many customers for the small number of AIEd companies to serve. This problem will likely solve itself as more companies get involved in the AIEd space and overall demand for AI platforms plateaus, but there are other problems that need to be addressed before widespread acceptance of AI in academia can happen.

Currently, most AI platforms are limited in their scope and are not capable of completing “higher level” tasks, which can be a benefit or drawback depending upon one’s perspective. AEId platforms are unable to locate obscure citations and possibly even more interestingly, they often cannot locate newer publications. Overall, AI is also not yet to the point where it can articulate deeper, abstract reasoning. This can be a hinderance to researchers who need to quickly publish, but a benefit for professors who teach upper level undergrad and graduate courses, as they know the papers will not be written by AI. It is also important to note that many AI programs are written with a self-censorship feature, so using these programs will create problems for papers about more controversial topics. 

The most obvious drawback that the widespread adoption of AIEd faces is the number of real and perceived ethical issues associated with the technology. Unfortunately, ethics is not a major concern for most of the education-technology (Ed-Tech) companies building AI systems, which some scholars believe can lead to a number of problems, including the following: the discrimination against some groups because of a lack of data, the lack of personal data protection, cheating by students and plagiarism by scholars, and a stigma placed on scholars who use AI, even properly. Many of these barriers to widespread adoption will likely be rectified in the coming years, opening new opportunities for universities, scholars, students, and even entrepreneurs. 

Future Trends of AI in Academia 

Like or hate AI, most who work in academia are coming to the realization that the technology will likely play some role in the space in the future. Based on what has already happened, how AI has affected other sectors, and what some notable tech researchers believe, there are a few notable trends to keep an eye on. First, expect classrooms to look very different thanks to AI. Watch for AI to assist, not replace professors, ultimately allowing them to give more time to students and to personalize lesson plans and assessments as mentioned earlier. 

As AI generally and AIEd in particular progress in the next few years, more universities will offer classes, majors, and possibly entire departments dedicated to AI studies. The University of Rochester as well as other tech-centric schools already offer classes on building AI systems, and as the technology becomes more accessible expect the “AI studies” trend to gain momentum, eventually making its way to public universities across the country. This trend could be vital to keep American students and workers competitive with an increasingly educated and global workforce.

Another important trend in AIEd to watch out for is students’ greater autonomy and control over their academic and personal data. Although the idea of “Big Brother” is inevitably raised during any conversation about AI, some experts argue that if properly used, and if students are adequately educated, then students can have more access and control over their data. As our world generally becomes more digitized, expect this to be one of the more important discussions surrounding AI in the coming years.

Finally, an important future AI trend to follow is its entrepreneurial opportunities. Investment in EdTech companies more than doubled in 2020 from the previous years, and as discussed earlier there’s now a bottleneck of consumers who want AIEd technology. Some major EdTech companies that are jumping into the AI space include Third Space Learning, which is a British company that uses AI learning apps such as Duolingo and Babbel in its curriculum. The long-established American EdTech company, Pearson, is transitioning to a digital format and new derives 66% of its revenue from digital products. Pearson also claims to have developed the world’s first AI powered calculator, Aida. 

The story of AI in academia is just starting to be written and it will no doubt contain several more chapters before it’s completed. AI presents many opportunities and a few drawbacks for university administrators, professors, researchers, and perhaps most importantly, the students. In order to be successful in the global marketplace all will have to learn about the nuances of and how to operate AI in the future, and universities that understand the trends and how to apply them to their students/consumers will likely see long-term success. 

About the author:

An industry leader and influencer – Rudly Raphael specializes in all aspects of research logistical design involving quantitative methodology,  implementing internal system infrastructure to streamline business processes, channelling communication and developing innovative research solutions to ensure Eyes4Research remains a competitive force in the marketplace. An entrepreneur, inventor (patent holder), blogger and writer – his articles have been published in various magazines such as Medium, Ebony Magazine, Bussiness2Community and also cited in various journals and academic publications.