Exploratory Learning in Statistics Education Through Generative AI: A Constructivist Approach with Case Studies

The integration of generative artificial intelligence (AI) into education presents a transformative opportunity to reimagine how statistics is taught and learned. This paper explores how tools such as ChatGPT and Julius AI can support exploratory learning in statistics education through a constructivist lens. Drawing on foundational theories of Piaget and Vygotsky, we present four practice-oriented case scenarios that illustrate how generative AI can scaffold student inquiry, clarify complex concepts, support statistical coding, and promote critical reasoning. Each scenario is designed to foster active, contextual, and reflective learning, positioning AI not as a shortcut to answers but as a catalyst for deeper understanding. The paper also redefines the role of the educator in this evolving landscape, emphasizing the shift from content delivery to the design of AI-enhanced learning experiences. By bridging theory and practice, this work contributes a novel pedagogical framework for integrating generative AI into statistics education and offers actionable strategies for educators. Future research is encouraged to empirically evaluate the impact of these approaches on student learning outcomes, engagement, and ethical AI use in the classroom.