Generative AI in Writing Assessment: Translating Research Into Practical Applications in Global Education

Abstract This chapter explores the application of generative artificial intelligence (AI) technologies in writing assessment, with a focus on translating current research into contextually responsive strategies for global implementation. Writing assessment plays a central role in identifying student needs, monitoring progress, and informing instruction, yet traditional methods frequently struggle with issues of scalability, consistency, and objectivity. Generative AI tools offer potential improvements by automatically evaluating key writing features such as grammar, coherence, argumentation, and creativity. Drawing on international case studies and comparative education frameworks, this chapter examines how AI-based assessment systems are being adopted and adapted across diverse cultural, linguistic, and infrastructural settings. Particular attention is given to the challenges of aligning AI tools with local pedagogical norms, ensuring equitable access in resource-constrained environments, supporting educator readiness, and mitigating algorithmic bias. These challenges underscore the complexity of implementing AI-driven assessment at scale and the urgent need for participatory, equity-focused design approaches. By synthesizing empirical research with practical insights, this chapter provides guidance for educators, policymakers, and developers seeking to integrate AI writing tools in ways that support inclusive, fair, and effective learning outcomes. The discussion concludes by identifying priority research questions and offering strategies for bridging the gap between innovation and practice within global education systems.