AI impact on academic research

Research in the early 1990s, involved weeks and months spent in libraries taking notes. By the late 1990s, digital bibliographies reduced this effort to a few weeks. Now, artificial intelligence (AI) programmes can summarise entire works in seconds and provide access to character analysis, theme overviews, and secondary sources.

This raises questions about the length and structure of academic research. Traditional research was deep and arduous, but AI has automated the mechanical work. For example, an AI programme can provide a synopsis, psychological insights, and an overview of critique in minutes for a scholar researching 21st-century responses to Hamlet.

Ph.D. research on the Black Mountain Poets once required travel, archives, and interaction with scholars. AI can now scan and analyse such archives in seconds. Gathering biographical details and understanding personal challenges of a historical figure, which once took days or weeks, can now be compiled instantly with AI tools like Google Lens and natural language processors.

An NIT professor stated that AI had saved him at least 15 years of academic labour. In literary studies, AI facilitates cross-textual analysis, identifies intertextual relationships, and quickly offers historical context. Summaries, translations, and bibliographies are widely available, allowing scholars to focus more on interpretation and synthesis. The time saved from data collecting can be used for higher-level thinking and creative analysis.

Limitations of AI

AI cannot replace human intelligence, empathy, or interpretive nuance. Literary research involves ambiguity and unique interpretations, which are human tasks. Overdependence on AI can lead to conceptual shortcuts, potentially misinterpreting analogies or overlooking subtle themes. Authentic research thrives on depth and intellectual engagement. Rushing the process risks losing the richness of academic pursuit.

Rethinking Research

Instead of resisting AI, we must rethink research, focusing on generating new knowledge and developing new interpretations. The researcher’s function is transitioning from data collector to meaning maker. Critical thinking, imagination, and questioning must be prioritised. Academic training must prepare students to use AI tools wisely, and institutions must re-evaluate traditional research models.

The fundamental component of scholarship remains unchanged. Critical thinking, intellectual rigour, and creative insight remain central and uniquely human. The key issue is how we wisely use the time available. AI frees us from routine tasks, encouraging deeper thought. The purpose of contemporary research should be to do it better.