Abstract
Artificial intelligence (AI) is transforming scientific research by accelerating literature retrieval, enhancing evidence synthesis, generating hypotheses, and supporting scholarly communication. Unlike earlier technological innovations that primarily extended human capabilities, AI increasingly performs cognitive tasks traditionally associated with scientific reasoning and knowledge creation. While these advances improve research efficiency and interdisciplinary discovery, they also raise important questions about the long-term sustainability of scientific knowledge. This perspective explores how the growing integration of AI into scientific workflows may reshape the way knowledge is created, interpreted, and validated. It introduces the concept of recursive epistemic drift, describing the gradual movement of scientific understanding away from direct empirical observation toward successive layers of AI-mediated interpretation. Although AI can identify patterns and synthesize vast amounts of information, it cannot replace the uniquely human capacities for critical inquiry, contextual judgment, conceptual imagination, and reflective skepticism that underpin scientific progress. These epistemic capabilities remain essential for questioning assumptions, recognizing anomalies, and ensuring that scientific knowledge remains self-correcting and continually renewed. From a knowledge management perspective, the paper argues that the future sustainability of scientific knowledge depends not only on advances in AI but also on preserving the human intellectual capabilities that continually reconnect scientific understanding with empirical reality.
