Artificial Intelligence-Driven Tools as Catalysts for Teaching and Learning Improvement in Economics Education

Abstract

Artificial intelligence (AI) driven tools ranging from intelligent tutoring systems and learning analytics to generative AI and automated feedback are increasingly shaping teaching and learning across economics education. This paper synthesizes contemporary research and policy guidance to explain how AI functions as a catalyst for instructional effectiveness, learner engagement, differentiation, and assessment, while also examining risks related to bias, privacy, academic integrity, opacity, and educational inequality. Drawing on learning sciences and socio-technical perspectives, the paper proposes a human-centered framework for responsible AI integration across pedagogy, assessment, learner support, teacher professional practice, and governance. It argues that AI yields the greatest educational value when deployed as an augmentative resource that strengthens teacher decision-making and learner self-regulation rather than as a substitute for professional judgment.