Artificial intelligence (AI) is rapidly transforming how knowledge is produced, taught, and applied across disciplines. This chapter explores the integration of AI into the teaching and empirical research of welfare economics, with a particular focus on inequality, poverty, and distributive justice. Drawing on theoretical contributions from Amartya Sen, Martha Nussbaum, and Anthony Atkinson, the text examines how AI tools—such as intelligent tutoring systems, machine learning models, and fiscal microsimulations—are being used to enhance educational practices and improve policy evaluation. Through real-world case studies and interdisciplinary reflections, the chapter highlights the opportunities and ethical challenges posed by AI in this domain. The discussion emphasizes the need for inclusive pedagogies, transparent data practices, and critical engagement with algorithmic tools in shaping future welfare economists and evidence-based policies.