Artificial intelligence (AI) is fundamentally reshaping research and information science by enabling faster data processing, more nuanced analysis, and new methodological possibilities that were previously unattainable. Its integration into research workflows enhances knowledge discovery, supports more equitable and ethical inquiry, and broadens the scope of what scholars can investigate across digital, social, and technical domains. As AI tools advance, they not only boost efficiency but also challenge researchers to rethink issues of bias, trust, and responsibility within data‑driven environments. These shifts are influencing how information is created, organized, and interpreted, with wide‑ranging implications for society. AI-Driven Research Innovations in Computing and Information Science examines emerging AI-driven research methods and frameworks across computing, information science, and social contexts. It explores innovative applications, ethical considerations, and field-specific methodologies to guide researchers in navigating an increasingly AI-integrated world. Covering topics such as algorithmic injustice, digital labor platforms, and school libraries, this book is an excellent resource for researchers, academicians, graduate and postgraduate students, industry practitioners, data analysts, library and information professionals, policymakers, and more.