Exploring on the Paths and Effectiveness of AI-enhanced Personalized Teaching in Experimental Design and Analysis - Dang, Jiatao; Yang, Shaoqi; Xu, Lianchao; Wang, Wei; Hu, Jianjun (Journal of Educational Research and Policies, 2025)
The Experimental Design and Analysis course often faces challenges in addressing individual students needs due to large-class teaching instruction. Personalized teaching offers a viable solution by accommodating the diverse academic backgrounds and learning conditions of students. This paper analyzes the advantages of personalized teaching and proposes the use of artificial intelligence to empower personalized instruction in Experimental Design and Analysis. By leveraging AI’s strengths in information collection, data analysis, and comprehensive evaluation, tailored teaching methods can be designed for the course. Integrating AI into daily teaching and management can enhance learning efficiency, cultivate students’ research skills, and better achieve the course’s teaching objectives.