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.

Authors:
Yuh-Jen Wu, Tzu Chi University, Taiwan
Chun-min Lin, Tzu Chi University, Taiwan

About the Presenter(s)
Dr. Chun Min Lin is currently an assistant professor at the Marketing and Distribution Management Department, Tzu Chi University of Science and Technology(TCUST) in Hualian, Taiwan. Dr. Lin was educated at the Texas Tech University in Lubbock, Texas, U.S.A. and graduated with a Master’s Degree. Subsequently, he was educated at National Dong Hwa University in Taiwan, and graduated with a Doctoral Degree. Dr. Lin started his academic career in Taiwan Hospitality and Tourism College since 1998. During such time he has been a director of the library for 15 years and supervised the development and establishment of the library automation system. Dr. Lin is currently a member of Interlibrary Cooperation Association of the Republic of China.

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