AI for Research Volume 2 : Quality, Impact, and Implementation

Omtale

Artificial intelligence is transforming every aspect of research practice. Researchers face a fragmented landscape of tools, evolving ethical requirements, emerging regulations (notably the EU AI Act), and rapidly shifting best practices. The second of two volumes, this book provides a comprehensive treatment of artificial intelligence across the complete research lifecycle—from initial ideation through analysis, quality assurance, dissemination, and long-term impact. Key FeaturesThis book is the only resource covering AI applications across the entire research journey, from initial idea through long-term impact. It systematically applies four major AI governance frameworks (RUAIH, FUTURE-AI, NIST AI RMF, and GMLP) throughout both volumes, showing researchers how to operationalize abstract principles in daily practice. It includes detailed analysis of the EU AI Act implementation timeline (February 2025 through August 2027) with specific guidance for research applications. It helps researchers and institutions understand high-risk classifications, research exemptions, and compliance strategies before requirements take effect. It moves beyond hype and fear to deliver a balanced, practical approach to human-AI collaboration. Each chapter includes "Capabilities and Limitations" analysis, ethical considerations, and implementation strategies that acknowledge both AI's transformative potential and its persistent limitations. This book features concrete examples demonstrating implementation principles in practice. Each chapter concludes with practical implementation strategies, reflection questions, and guidance applicable to institutions of varying sizes and resources. Volume 1 explores how AI can improve essential research activities, including designing studies, securing funding, navigating compliance and ethics reviews, recruiting participants, managing operations, collecting data, and analyzing results. Volume 2 continues the exploration of quality assurance, dissemination, peer review, impact, and the necessary institutional changes to responsibly implement AI capabilities.