When OpenAI launched ChatGPT to the general public on November 30, 2022, it left an indelible mark on the future of technology, artificial intelligence, and also humanistic and academic engagements. When academic institutions had gotten ahold of this new technology, it caused widespread paranoia regarding the probable unethical usages that would arise on university grounds. If students could generate text, with a decent research base, for free, how would the institutions ascertain the authenticity and validity of the theses and research papers being presented?
From Plagiarism to Incompetence
The paranoia stemming from the advent of generative AI in academic spaces was not unwarranted. It did not take long for students to use ChatGPT to generate their essays and assignments for their coursework. On the flip side, seasoned academicians also utilised generative AI, albeit much differently from their students, but the core idea remained the same: Human academic labour was being compromised for automated output. While students were plagiarising essays, teachers were using the same AI tools to check for plagiarism until these endeavours eventually became ironic.
Institutions had begun to block ChatGPT and other generative AI software from school networks and Wi-Fi and students who were caught “cheating” were promptly reprimanded. However, plagiarism has existed before the advent of generative AI. One might even argue that it was an ancient art. Like with most modern technology, the sheer connectivity of networks has allowed this practice to become more accessible and widespread. But has there ever been a student who has not asked their parents for help with their homework? Or students who have not written paragraphs word-for-word from some dusty, niche, impermeable book from the murkiest depths of the library for a simple essay on global warming? If ChatGPT does not offer anything different from the traditional forms of plagiarism then why do students feel the need to use such tools for their academic engagements?
Times change, values, in most cases, do not. It is a fact universally acknowledged that in the digital contemporary world, academic pressure has greatly escalated with the sporadic increase in coursework. Familial and societal expectations can weigh down pupils. The quest for the “perfect grade” is no longer a literary trope limited to the stereotypical character of the brainiac-with-glasses, but is now a necessity in contemporary society. Cheating and plagiarism correspond to much deeper, psychological systems and cannot be equated to mere incompetence without analysing the core issues.
The Core Issues: Institution of the Institutions
When John Newmann proposed the “Idea of a University” in the 1850s, he intended for research to be an entity separate from the university. It is remarkable to consider the extent to which modern academic institutions have evolved to a point where research and higher education are synonymous. Richard Heller in his text, “The Problem with Universities Today”, identifies managerialism as the core issue of the modern university. With universities placing managers in the hierarchy, there is a stark shift from teaching to research, and eventually to managerial tasks. In other words, academic workload has greatly increased and the liberty of the researcher is reduced to a dubious concept. Such issues amplify as one moves down the academic hierarchy toward the students who make up the base of the institution.
Business incentives and the commodification of education have also impacted the university model. When survival in a profit-driven society becomes the main concern of academics, the purpose of modern research must be treated with discretion. Does academia retain its position as the sole prophet of knowledge or has it become a business? The latter seems to be the unfortunate reality.
A Double-Edged Sword
Rather than focusing on what AI should not be used for, perhaps we ought to shift our focus toward what AI could be used for within academia and research. However, it is imperative that we consider the fact that AI tools are not devoid of falsities. Writing your entire dissertation based on what ChatGPT has generated might not be a good idea unless one is well-versed in their area of research. Utilising such tools for trend prediction or data organisation might be a better idea. Julius and GPT 4 particularly excel at data analysis and visualisation. For humanities scholars dabbling with quantitative data analysis and its seemingly endless ocean of technical jargon, such software is a godsend.
Rather than having AI tools draft the paper, it would be better to employ them in the search for background information and materials for the literature review. The internet is a large repository of data and research can become overwhelming at times. AI’s ability to compose precis would not only be time-efficient but can assist tremendously in the research process for scholars with disabilities. Tools such as AskYourPDF can generate succinct and precise summaries of academic articles within seconds.
AskYourPDF
Scholars tend to forget that some of their most beloved tools operate using AI. In academic spaces, Grammarly has become synonymous with editing and grammar-checking. It can polish most manuscripts by offering several suggestions based on general grammar rules. However, for certain contextual aspects, scholars would have to use their own judgement.
Grammarly
Outlandish it might seem but in recent years people have begun generating code using AI. This has largely democratised the process for the general public, especially for those scholars who do not possess prior knowledge of programming but require code for their research. Github Copilot is a widely utilised AI developer tool that has garnered industry recognition for its abilities.
Professors are not neglected either. There are tools that allow the user to detect the inclusion of AI in any written text. Google Classroom has a built-in originality checker and services such as TurnItIn and Winston AI prevent generated works from being marked as original compositions.
Moderation in the usage of such tools is the key to streamlining one’s research process. Tempted to copy that AI generated literature review? Unless you are fine with inaccuracies and discrepancies in the text, that might not be the best course of action. Researchers must be aware of the benefits and limitations of using AI within research. Scholars could analyse summaries of research articles to develop the arguments of their research. Additionally, one should use AI-tools to edit and cite rather than to compose. This does not entail the academic disintegrity of scholars; They would still conduct preliminary research to identify any possible discrepancies. These tools should be used to assist the users in the process, not to devise the end-product for them.
Conclusion
The usage of AI in academic research and other engagements has undoubtedly become a point of contention in recent years. Ethical concerns such as plagiarism and incompetence have greatly littered the scholarly domains however, such concerns are often handled superficially. The question of plagiarism corresponds to deeper, core issues in the conception of the University. When education and research have become commercialised pursuits and academic roles have been riddled with micro-responsibilities, the integrity and prestige of scholars have diminished. Managerialism will only accentuate going forward and in order to contest that research tools must harness the benefits of AI. Rather than only focusing on the unethical sides of AI usage in academia, we ought to highlight the ethical uses of it to promote a healthier working environment for scholars in the larger scheme of things.
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