Research on Performance Evaluation of Robot-assisted Teaching Classroom Application

With the continuous development of artificial intelligence (AI) and education, AI robots have participated in classroom teaching, forming a new teaching mode that effectively increases the interest and improves the effectiveness of classroom teaching. At the same time, it also reduces the workload of teachers to a certain extent. Considering that the theoretical system of robot assisted classroom teaching evaluation is not yet sound, this study combines the Kirkpatrick evaluation model and Bloom’s taxonomy of educational objectives to establish a multi-level and diversified performance evaluation framework. The evaluation design research from four levels of perceptual expression, cognitive transfer, information processing and reflection summary. The data analysis focuses mainly on the experiential and effectiveness issues of teachers and students using robots in classroom teaching. A performance evaluation model and scale are constructed, and the questionnaire is designed to systematically collect the data of teachers and students using robots to assist classroom teaching, and to evaluate the changes in the level of performance development.