| Abstract: |
Artificial Intelligence (AI) and Machine Learning (ML) are transforming educational research through the integration of computational intelligence, data-driven analysis, and adaptive learning systems. This chapter explores the multifaceted applications of AI and ML in education, ranging from personalized learning environments to predictive analytics, natural language processing, and intelligent tutoring systems. The discussion extends to methodological and ethical considerations, emphasising the importance of transparency, fairness, and data privacy in the use of these technologies. It also highlights the challenges associated with algorithmic bias, interpretability, and scalability in educational contexts. Drawing upon contemporary research, the chapter articulates a conceptual framework linking AI's computational capacity with educational theory and practice, providing a critical perspective on how emerging technologies are reshaping pedagogy, assessment, and policy. The concluding sections envision the future trajectory of AI in education, underscoring the need for interdisciplinary collaboration, ethical governance, and human-centred design in educational AI systems. [This paper was published in: "Educational Research: Perspectives and Practices," edited by Dhriti Tiwari and Azkiya Waris, Book Rivers, 2026, pp. 126-133.] |