BECE. Newsletter 

Vol.17, No.1, 2024, BECE 1853 

 

 

 

 

Factors Influencing Teacher Satisfaction with 

Human-Machine Collaborative Teaching: A Study 

Based on Structural Equations and Fuzzy-Sets 

Qualitative Comparative Analysis 

By Fang, X., Xu, L., & Jiang, P. 
Correspondence to: Xu Fang, Nantong University, China. E-mail: 

blueskyxfang@126.com 

UMAN-MACHINE collaboration, as an important application of intel-

ligent technology, is of vital significance for the advancement of digital 

transformation in education. Amid the progress in human-machine collabora-

tive teaching, its practical effects and factors influencing teacher satisfaction 

with it are questions pending in-depth investigation. This study established a 

model of the factors relating to teacher satisfaction with human- machine 

collaborative teaching, based on the expectancy confirmation theory, motiva-

tion theory and information system success model, in combination with the 

analysis of interviews.  

The empirical research results showed: 

• Amusement perception was the necessary factor in the teacher’s 

continuance intention to adopt human-machine collaborative 

teaching. The information quality and system quality of human-

machine collaborative teaching were core factors for the teach-

er’s continuance intention to use it. 

• Among the four factors relating to teacher satisfaction with hu-

man-machine collaborative teaching, namely the expectancy con-

firmation, amusement perception, information quality, and sys-

tem quality, the expectancy confirmation had the greatest effect 

on it, whereas the amusement perception posed the slightest ef-

fect. 

• Among the three factors relating to the perception of the useful-

ness of human-machine collaborative teaching, namely the ex-

pectancy confirmation, information quality, and system quality, 

the expectancy confirmation had the greatest effect on it, while 

the information quality exerted the minimal effect. 

Based on research findings, the article makes suggestions as follows. 

• Innovate the human-machine collaborative teaching modality by 

integrating machine intelligence into classroom teaching to in-

H 

mailto:blueskyxfang@126.com


 

Vol.17, No.1, 2024, BECE 1854 

crease teacher-student, inter-student, and human-machine inter-

action. 

• Enhance the mutual support between teachers and machines to 

realize technology-enabled, individualized instruction and boost 

the professional level of teachers. 

• Increase intelligent instruction literacy of the teacher to fully lev-

erage the high efficiency of artificial intelligence technology and 

infuse AI into day-to-day teaching. 

• Establish partnerships between schools and AI experts to effec-

tively implement relevant human-machine collaboration technol-

ogies in teaching and engage external AI specialists to provide 

teachers with targeted training to improve the outcomes of hu-

man-machine collaborative teaching 

 
Source: Journal of Distance Education, 2024; 41(6):71-83.