The Impact of AI on Scientific Knowledge Production: Capabilities, Limits, and Implications

AI tools are now used in many everyday research activities, including literature searching and screening, data cleaning and coding, drafting manuscripts, translation, figure generation, and even aspects of peer review. In many research environments, these technologies have become part of the routine workflow rather than optional tools. Their influence extends beyond simply increasing efficiency; they are also changing how ideas are developed, how evidence is synthesized, and how research findings are communicated. This shift raises an important question for researchers and institutions alike: when does AI enhance the quality and integrity of knowledge production, and when might it introduce new risks or distortions? Challenges such as unverifiable outputs, biased information retrieval, flawed reasoning presented with confidence, and unclear accountability are no longer theoretical concerns. These issues have direct implications for research methods, transparency, reproducibility, and trust in the scientific record. The Impact of AI on Scientific Knowledge Production: Capabilities, Limits, and Implications focuses on the impact of AI on scientific knowledge production with a deliberately evidence-oriented stance. This book brings together conceptual work, empirical studies, and field cases that clarify what AI can support reliably, where current systems remain fragile, and what changes are needed in research practice, training, and governance. Covering topics such as epistemic blind spots, the AI validation gap, and reproducibility standards, this book is an indispensable academic resource for graduate and doctoral students, faculty members, academic researchers, research supervisors, peer reviewers, data scientists, information specialists, policymakers, and more.