Artificial Intelligence has revolutionized the way research is conducted across disciplines. From automating literature reviews to generating data insights and even drafting papers, AI tools are accelerating the pace of discovery while reshaping the researcher’s role. What once took weeks can now happen in hours.

This seismic shift prompts a critical question: Should we rethink the objectives, structure, and assessment of academic research in the age of AI?

Traditionally, academic success has been measured by originality, rigour, and the ability to manually synthesize complex ideas. But with AI assisting (or replacing) many of these steps, the focus may need to shift from how the work is done to why it’s done and how ethically and critically it engages with knowledge.

Moreover, AI raises concerns about bias, plagiarism, and over-reliance. As such, scholars and institutions must reflect: Are we equipping researchers to co-create with AI responsibly, or are we clinging to outdated standards?

In a world where machines can “think,” perhaps it’s time academia rethinks what it means to know.