The discourse surrounding artificial intelligence in higher education has settled into a comfortable, yet entirely predictable, binary. On one side, proponents herald a new era of efficiency; on the other, skeptics fiercely defend academic integrity, guarding against plagiarism and the erosion of the “human touch”. Both sides raise valid points, but if we are to have a truly holistic debate about the future of academia, we must put all the cards on the table. Currently, a crucial card is missing from the conversation: the biological limit of the human evaluator. When we frame the hesitation to adopt AI strictly as a noble defense of academic rigor, we inadvertently mask a profound structural inefficiency in academic labor. We do not need to pit AI Integrationists against AI Precautionists. Instead, we need to honestly examine what the traditional, purely manual system of evaluation costs us in terms of cognitive depletion and the inherent limits of human expertise.
Prince Sarpong (Sat,) studied this question.