Research was a time-consuming procedure in the early 1990s, when I was pursuing my M.Phil. in English lLiterature. Weeks and months were spent browsing library shelves, taking handwritten notes, and meticulously crafting arguments. Gathering even the most basic resources required time and perseverance. By the late 1990s, when I was doing my Ph.D., digital bibliographies replaced old card systems in libraries, cutting months of effort down to a few weeks.
Now that I am a research supervisor, I see a different world. Artificial intelligence (AI) programmes can summarise whole works — including novels, critical essays, and poetry collections — in seconds and provide immediate access to character analysis, theme overviews, and pertinent secondary sources. This technological breakthrough poses significant questions: Should research still take years in an era when foundational data is instantaneously available? Should we reconsider the objectives and structure of academic research?
What AI can do
Traditional research was deep and arduous. Reading ought to be in-depth and sustained. However, with AI, most of the mechanical work has been automated. For example, a scholar researching 21st-century responses to Hamlet can instruct an AI programme to provide a synopsis, psychological insights, and an overview of growing critique in minutes. My Ph.D. research on the Black Mountain Poets required me to travel across the U.S., consult archives and original manuscripts, and interact with scholars. As a Fulbright scholar, I had the privilege of visiting Black Mountain College and engaging with source material that had rarely been explored. AI can now scan and analyse such archives in seconds.
Consider a student studying a historical figure. Previously, gathering biographical details, appraising social achievements, and comprehending personal challenges required days or weeks. AI tools such as Google Lens and natural language processors can now compile and format such data instantly. Provide a clear prompt, and the necessary material will arrive, frequently with references and structure already in place. At a recent symposium on AI in education, an NIT professor stated that AI had saved him at least 15 years of academic labour. This statement encapsulates the massive transition we are witnessing.
In literary studies, it facilitates cross-textual analysis, aids in the identification of intertextual relationships, and quickly offers historical context. Summaries, translations, and bibliographies are widely available, allowing scholars to focus more on interpretation and synthesis. Previously, a PhD may last three-to-five years and focus on a particular topic, but scholars today can investigate many themes or collaborate across fields. The time saved from data collecting can now be used for higher-level thinking and creative analysis.
What AI can’t do
However, AI has its limitations. It cannot replace human intelligence, empathy, or interpretive nuance. Literary research is more than mere summary of content; it is also about dealing with ambiguity, comprehending historical and cultural contexts, and providing unique interpretations. These are essentially human tasks.
Overdependence on AI can lead to conceptual shortcuts. Students may avoid the hard effort of intensive reading and critical engagement, resulting in shallow understanding. AI may potentially misinterpret sophisticated analogies or overlook subtle themes, resulting in generalised responses that miss the essence of the issue. Authentic research thrives on depth, paradox, and a sustained intellectual engagement. If the process is rushed, we risk losing the richness of this academic pursuit.
Instead of resisting these developments, we must rethink research. Its core focus has always been generating new knowledge, developing new interpretations, and contributing meaningfully to academic discussions. The process of gathering information is just the beginning. The researcher’s function is transitioning from data collector to meaning maker. In this new world, critical thinking, imagination, and the willingness to question must be prioritised. Academic training must prepare students to use AI tools wisely, without letting them control the outcome.
Institutions too, must re-evaluate their traditional research models. Is the worth of a Ph.D. determined solely by its duration, or by the breadth and uniqueness of its contribution? Could shorter, more targeted undertakings facilitated by AI, be equally impactful?While AI has transformed research by making it faster, easier, and more collaborative, the fundamental component of scholarship remains unchanged. Critical thinking, intellectual rigour, and creative insight remain central and uniquely human. As a research supervisor, I feel that the key issue is not whether research should span years, but how we wisely use the time available. AI frees us from routine tasks, encouraging us to go further and think deeper. Thus, the actual purpose of contemporary research should not be to accomplish more, but to do it better.
The writer is Professor of English and Dean of Student Affairs, Sahrdaya College of Advanced Studies (Autonomous), Kodakara, Thrissur, Kerala.
