Framework for Integrating Generative AI into Statistical Training in Doctor of Education Programs

Abstract

This paper proposes a framework for integrating generative artificial intelligence (AI) tools into statistical training for Doctor of Education (EdD) students. The rigorous demands of doctoral education, coupled with the challenges of learning complex statistical software and coding language, often lead to anxiety and frustration among students, particularly those in part-time or online programs. This article explores how generative AI can serve as a scaffold for learning, potentially mitigating statistics anxiety and enhancing students' abilities to focus on core statistical concepts rather than software intricacies. The proposed framework, grounded in constructivist learning theory, outlines a process for faculty to facilitate dialogues using generative AI tools that support students in developing research questions, selecting appropriate statistical tests, checking assumptions, and conducting statistical analyses. By leveraging AI as a dialogic partner, students can engage in self-regulated learning and enhance critical thinking skills essential for practitioner-scholars. This approach has the potential to improve statistical training in EdD programs, producing more competent translators of research who can effectively apply and interpret statistical methods in their professional practice. The article concludes by discussing implications for EdD programs and suggestions for improving the curriculum that includes statistical training.