Understanding Scholarship: The Role of Large Language Model AI in Modern Academic Practice
Examining the Utility and Limitations of AI in Scholarly Work
Note: This essay was prepared with the research assistance and ghostwriting of ChatGPT 4.0.
Preface
In the modern academic landscape, the emergence of Large Language Model (LLM) AIs like ChatGPT presents new challenges and opportunities. For centuries, scholarship has followed a process that was time-consuming, rigorous, and, in theory, thorough. Scholars would read extensively, analyze texts, and produce work that contributed to the collective knowledge of their field. However, the introduction of LLM AI has shifted this balance. Does using AI to assist in the creation of academic work compromise the integrity of scholarship? Does the end product, if correct, justify the method? These are the questions that prompted this essay.
Introduction
Scholarship, at its core, is about the pursuit of knowledge through research, analysis, and synthesis. Traditional scholarship theoretically adhered to a process: the scholar would form a question, consult existing literature, critically assess it, and then generate an original argument, supported by evidence (Brown et al., 2020). Inline citations served to justify the argument, referencing works that were supposedly vetted and read thoroughly (Vollmer, 2020). In this sense, the process was as important as the final product, ensuring that scholarship was grounded in research and thoughtful analysis.
But with the rise of LLM AIs, like ChatGPT, we are faced with a new reality. These models can rapidly compile large amounts of information and produce written content based on prompts (Brown et al., 2020). There is an opportunity to use AI as both research assistant and ghostwriter. However, this shift raises important epistemological issues. Is the work still credible if produced by AI? Is it more or less credible than strictly human based research? And does the correctness of the final product render the method irrelevant (Floridi & Cowls, 2019)?
The Traditional Scholarship Process
Historically, scholarship developed through a structured process. A scholar would begin with an idea or question, pursue relevant references, and write up their findings, citing the work of others to lend credibility to their arguments (Vollmer, 2020). In this approach, there was an assumption that all cited materials had been thoroughly vetted by the scholar, who had read and understood them.
But let’s be honest: even within this traditional framework, much of the material scholars encounter is unreliable. Academics, much like the rest of us, sometimes produce "bullshit"—works that are dramatically or subtly wrong (Goldacre, 2014). This problem is not new to AI; it’s an epistemological issue that’s been around for as long as people have been writing. The challenge has always been discerning what is true and what is false, a task made even more difficult in the digital age, where vast amounts of information are available at the click of a button (Floridi & Cowls, 2019). Also, the LLM AI are themselves created by incorporating human productions into the AI machine, so errors in that become errors in the AI output. There is no way around this.
Enter AI: Reversing the Process
Large language model AIs change the game. Instead of a scholar going to the library, reading books and journals, and synthesizing information manually, LLM AIs can aggregate this data in seconds (Brown et al., 2020). The AI doesn't just pull sources—it produces a cohesive narrative, essentially ghostwriting on behalf of the user. This reverses the traditional process of scholarship, where the scholar was the active agent, gathering information and constructing their argument (Floridi & Cowls, 2019).
LLM AI has access to a much larger body of information than any single scholar could ever hope to process in a lifetime. This can be seen as both an advantage and a risk. While the volume of information is large, not all of it is reliable. AI draws from human-generated content, which, as mentioned, can be riddled with mistakes, biases, and propaganda (Vollmer, 2020). The algorithm doesn’t know what’s true or false; it simply assembles the information.
The Epistemological Dilemma
This brings us to the epistemological crux of the issue: can we trust scholarship produced with the help of AI? In traditional research, the scholar would (at least in theory) critically assess their sources, looking for supporting and refuting evidence (Goldacre, 2014). AI cannot do this in the same way. It merely aggregates, presenting what it believes to be the most relevant information based on its training. But AI doesn’t “believe” anything—it lacks consciousness and the capacity for judgment.
Moreover, there's an underlying assumption in traditional scholarship that the scholar actively engages with opposing viewpoints. AI, by contrast, doesn’t have this critical faculty. It doesn't challenge its sources or even recognize bias—it just outputs based on patterns. In this sense, the role of the scholar is even more crucial: human oversight is needed to vet AI’s output, to ensure that what it produces aligns with the rigor of scholarly work.
Scholarship in the Age of AI
The world of academia is changing, and with it, the nature of scholarship itself. LLM AI has the potential to revolutionize research by speeding up the process of gathering information and producing written work (Floridi & Cowls, 2019). However, it also presents a challenge to the integrity of that work. Scholars today must grapple with how much they want to rely on AI and whether the final product still carries the weight of human investigation, critical thinking, and original thought (Brown et al., 2020).
Perhaps the answer lies in redefining scholarship for the modern age. If we accept that AI will play an increasingly large role in research, then scholars must adapt, not by abandoning the old methods but by incorporating AI as a tool—while maintaining the rigour (or pretense of such) that has theoretically defined scholarship for centuries.
Summary
The introduction of LLM AI has shifted the balance in academic scholarship. While AI offers the ability to process large amounts of information and produce written content quickly, it also raises significant epistemological concerns. Traditional scholarship was based on the idea of thorough research, critical analysis, and a rigorous vetting of sources. AI reverses this process, turning scholars into consumers of AI-curated information. This shift forces us to ask: Does the end product matter more than the process? And is the use of AI in scholarship a step forward, or a departure from academic rigour?
As the role of AI in scholarship grows, it’s clear that the traditional methods of research and writing will need to evolve. Whether AI can be fully trusted to produce reliable academic work is still an open question, but what is certain is that scholars must be vigilant, maintaining critical oversight over the use of AI in their work. Not much different than the curent status quo in othe words.
References
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language Models are Few-Shot Learners. arXiv. Retrieved from https://arxiv.org/abs/2005.14165
This paper, foundational in the field of AI research, discusses how large language models like GPT-3 can perform tasks with minimal examples (few-shot learning). It illustrates the model’s ability to generate human-like text and outlines the implications of AI in natural language processing, which is critical for understanding the use of LLMs in academic and scholarly contexts.Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review, 1(1). Retrieved from https://doi.org/10.1162/99608f92.8cd550d1
This article presents a framework for the ethical use of AI in society, introducing five core principles: beneficence, non-maleficence, autonomy, justice, and explicability. These principles are vital for considering how AI technologies should be integrated into academic research and scholarship, ensuring that AI is used responsibly and ethically in these fields.Goldacre, B. (2014). Bad Pharma: How Medicine is Broken, and How We Can Fix It. London: Fourth Estate. Retrieved from https://www.amazon.com/Bad-Pharma-How-Medicine-Broken-ebook/dp/B008PCVGKI
While not directly focused on AI, this book is a scathing critique of the pharmaceutical industry, revealing how biases and misinformation can corrupt scientific research. The same critical lens can be applied to AI-generated content, underscoring the importance of vetting AI-generated academic work for accuracy and reliability.Vollmer, M. (2020). Artificial Intelligence — How Will AI Change Our World and What Rules Do We Need? Medium. Retrieved from https://medium.com/@marcellvollmer/artificial-intelligence-how-will-ai-change-our-world-and-what-rules-do-we-need-f9505cb16129
This article explores the transformative role AI is playing across various sectors, including research and academia. It discusses the regulatory and ethical frameworks needed to govern AI use, which is critical for understanding how AI can be responsibly employed in scholarly work.