Abstract

While generative AI has been embraced as a powerful tool for research, its integration into academic workflows has paradoxically undermined productivity in several ways. This essay explores four key challenges: secondary source of anxiety, where overreliance on AI-generated summaries fosters doubt about source credibility and scholarly rigor; operational inefficiency, as AI often generates redundant or misleading outputs that necessitate extensive verification; the hidden labor of AI integration, requiring researchers to develop new skills and oversight mechanisms to manage AI-driven processes; and ethical ambiguity, as evolving AI capabilities blur the boundaries of authenticity, originality, and responsible scholarship. These issues are further compounded by diverging institutional and cultural perspectives on what constitutes legitimate intellectual contribution. As AI continues to evolve, so too must the research communities’ strategies for its responsible use—ensuring it functions as a tool for discovery rather than a bottleneck of informational chaos that stifles innovation.