Archives to Algorithms: Rethinking Academic Research in the Age of AI
Research in the early 1990s was a time-consuming and rigorous task. Pursuing an M.Phil. in English Literature required weeks and months of combing through library shelves, taking handwritten notes, and carefully building arguments. Even gathering the most basic sources demanded immense time and perseverance.
By the late 1990s, digital bibliographies and library catalog systems began to replace card indexes, reducing months of groundwork to a matter of weeks.
Now, as a research supervisor, the author sees a dramatically changed landscape. Artificial Intelligence (AI) programs can now summarize entire works including novels, critical essays, and poetry collections in mere seconds. These tools offer immediate access to character analysis, historical contexts, and relevant secondary sources. While this technological advancement holds vast potential, it raises an important question:
Should academic research take years when information is instantly accessible?
What AI Can Do
Traditionally, research demanded deep inquiry and sustained effort. Today, many mechanical and time-consuming tasks have been automated. For instance, a scholar working on 21st-century literature can now quickly receive summaries and insights generated by AI, rather than spending months immersed in archives.
As an example, the author shares his own Fulbright experience of traveling across the U.S. to consult primary manuscripts on the Black Mountain Poets. Today, such archival access can be partially replaced by AI tools that collate, summarize, and synthesize data quickly.
AI also assists students in tasks such as gathering biographies, appraising social contexts, and framing research outlines. Tools like Google Lens and ChatGPT can help compile structured material almost instantly, helping students and researchers spend more time on analysis and synthesis rather than just data collection.
What AI Can’t Do
Despite its strengths, AI has limitations. It cannot replicate human intelligence, empathy, or nuanced interpretation. Literature, as the author explains, involves more than summary it’s about understanding emotion, culture, and complexity.
True research also involves asking conceptual and philosophical questions tasks AI is unequipped to handle. Scholars must engage in critical analysis, debate, and original thought aspects that remain uniquely human.
There is also the risk of over-dependence on AI, which may lead to shallow understanding. Misinterpreting AI-generated summaries as complete insights could dilute academic rigour.
Shifting Roles and Future Possibilities
The role of the researcher is no longer confined to data collection. In the AI-driven world, researchers transition from being collectors to interpreters and creators of meaning. Literary studies now benefit from quick access to references, historical contexts, and intertextual analyses freeing up time for deeper intellectual work.
Institutions must now reconsider how Ph.D. research is evaluated. Is value measured by duration, or by the novelty and depth of contribution? As AI enables quicker and more targeted outputs, we must redefine what meaningful research looks like.
Ultimately, AI should be used as a tool not a crutch. It enhances research, but must not replace human curiosity, insight, and imagination. The future of academic research lies in using these tools wisely while preserving critical thinking, intellectual rigour, and creativity.
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