A0445
Title: Adapting student performance evaluation in the AI era
Authors: Suhwon Lee - University of Missouri (United States) [presenting]
Abstract: The integration of artificial intelligence (AI) into statistical education is changing learning methodologies and offering transformative opportunities for both educators and students. AI's impact on statistical education is evident in its ability to provide tailored learning experiences. Machine learning algorithms and intelligent tutoring systems offer personalized feedback and assessments, identifying areas of difficulty for individual students. AI-powered statistical software assists students in handling complex datasets, enabling them to focus on conceptual understanding rather than computational intricacies. Collaborative learning experiences are facilitated through AI-driven platforms equipped with natural language processing capabilities. Despite these advancements, ethics should be considered when adopting AI in statistical education. Addressing concerns related to data privacy, algorithmic bias, and the risk of overreliance on AI tools is crucial for responsible implementation. Additionally, continuous professional development for educators is essential to maximize the effective integration of AI technologies into teaching practices. The purpose is to explore the key aspects of AI adoption in education, focusing on personalized learning experience and collaborative platforms.
