Author Information
Yuh-Jen Wu, Tzu Chi University, TaiwanChun-min Lin, Tzu Chi University, Taiwan
Abstract
In the era of generative AI, a pressing question in higher education is which forms of statistical knowledge must be internalized in students’ long-term memory to support understanding, critique, and creative application. While AI tools can instantly provide answers, they cannot substitute the mental frameworks that enable learners to connect concepts and evaluate results critically. This study aims to identify and categorize essential knowledge in introductory statistics that should remain memorized despite the availability of AI support. The research adopts a two-step methodology. First, a Delphi process with university statistics instructors will establish consensus on a list of essential knowledge points, distilled from curricula, textbooks, and professional experience. Second, these knowledge points will be classified using Bloom’s taxonomy, with particular emphasis on distinguishing knowledge that must be remembered and understood from knowledge that can be effectively applied or evaluated with AI assistance. This approach contributes to clarifying the balance between human memory and technological support in statistical learning, providing implications for curriculum design and assessment in AI-enhanced learning environments.
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