Co-Creating the Future with Generative Ai: Exploring Ai's Capacities and Potential Incorporation in Statistical Education

Abstract

This paper contributes to the field of statistical science education by reviewing strategies for incorporating generative AI (GenAI) and assessing its suitability in various educational contexts. The proposed Co-creation approach emphasizes collaborative efforts between educators and students. The study evaluates six general strategies for dealing with GenAI, introduces assessment and teaching strategies, and explores GenAI's roles for students. Practical assessments using GenAI tools (ChatGPT and Claude) inform critical summaries of advantages and disadvantages in statistical tasks. Section three assesses the suitability of strategies, highlighting staged and process-oriented assessments, group assessments, and others as highly or partially suitable. GenAI is found to be most suitable as a mentor for students, partially suitable as a tutor and teammate, and unsuitable for roles such as oral assessment or higher-order test generation. The research underscores the need for comprehensive participation from both educators and students for the effective integration of GenAI in statistical education. The findings provide valuable insights for institutions and students, emphasizing the importance of structuring regulations and policies.

Keywords: Generative AI, ChatGPT, Statistical Science, Education