This introductory chapter presents the conceptual foundation and organizational framework for the edited volume, which explores the integration of artificial intelligence (AI) in STEM education research. It outlines the motivation for the book, emphasizing the increasing presence of AI technologies in education and the urgent need for STEM education researchers to engage with these tools both methodologically and critically. The chapter emphasizes key distinctions between using AI as a research method versus as a pedagogical tool and highlights the potential of AI to enable large-scale, multimodal, and dynamic analyses of learning. It positions the book as a resource that addresses both technical and epistemological questions, such as how AI can advance and support assessment practices and instructional innovation. The chapter defines the emerging key concepts of AI in STEM education research and introduces the structure of the book, briefly summarizing each chapter’s contributions across domains, including automatic scoring, automatic assessment generation, adaptive learning, adaptive testing, computer vision, knowledge tracing, intelligent tutoring systems, recommender systems, sentiment analysis, and explainable AI. Ultimately, the chapter advocates for interdisciplinary collaboration and ethical considerations, urging researchers to adopt responsible, inclusive, and theory-driven approaches to integrating AI. By doing so, the book aims to guide scholars in harnessing AI to advance STEM education research while remaining critically aware of its limitations and implications.