Content area
Background
Artificial intelligence has gradually been used into various fields of medical education at present. Under the background of moxibustion robot teaching assistance, the study aims to explore the relationship and the internal mechanism between learning engagement and evaluation in three stages, preparation before class, participation in class, and consolidation after class.
Methods
Based on the data investigated in 250 youths in university via multistage cluster sampling following the self-administered questionnaire, structural equation model was built to discussing factors of study process about moxibustion robots.
Results
It was found after moxibustion robot teaching assistance that preparation before class, participation in class and consolidation after class positively predicted learning engagement. Learning engagement, preparation before class, participation in class, consolidation after class positively predicted effect evaluation. Learning engagement played a mediating role in the effect of preparation before class and consolidation after class on evaluation.
Conclusion
Employing artificial intelligence in three stages of class can improve the quality and efficiency of medicine education and promote its innovation and development. Serviceable and valuable reference and inspiration for future teaching improvement and industrial development can be provided via the systematic research and analysis of the practical application of moxibustion robot in teaching.
Details
Educational Opportunities;
Independent Study;
Educational Resources;
Error Correction;
Influence of Technology;
Experimental Teaching;
Educational Technology;
Cognitive Style;
Instructional Materials;
Innovation;
Biotechnology;
Course Objectives;
Artificial Intelligence;
Student Motivation;
Data Analysis;
Language Processing;
Data Processing;
Educational Environment;
Course Content;
Cognitive Structures;
Learner Engagement;
Educational Facilities Improvement;
Educational Strategies;
Algorithms
Robots;
Educational technology;
Data analysis;
Physical therapy;
Medical students;
Research & development--R&D;
Feedback;
Efficiency;
Innovations;
Big Data;
Machine learning;
Acupuncture;
Artificial intelligence;
Knowledge;
Personalized learning;
Design;
Algorithms;
Cross-sectional studies;
Traditional Chinese medicine