Acceptance of Smart Teaching Tools and Its Influencing Factors Among University and College Teachers

College teachers’ acceptance of smart teaching tools affects whether they can make effective use of such tools while teaching, with the goal of materializing deep integration of technology and teaching activities. This article constructs a hypothetical model of college teachers’ technology acceptance path for smart teaching tools based on the UTAUT model and the revised IS/IT acceptance and utilization model. This research designed a questionnaire sent out to Chinese college teaching staff with the framework consisting of Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions. This work utilized the structural equation model (SEM) method to explore the relationship among Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions and Attitude, Behavioral Intention, and Using Behavior. The analysis results reveal that Performance Expectancy and Facilitating Conditions positively affect Attitude, Effort Expectancy and Social Influence negatively affect Attitude; Performance Expectancy positively affects Behavioral Intention, while Effort Expectancy negatively affects Behavioral Intention. However, Attitude and Facilitating Conditions positively affect Using Behavior. The above analysis suggests that colleges and relevant smart education industries, should: optimize smart teaching tools to improve their intelligence levels; implement smart teaching training to create favorable conditions; and encourage staff to further develop the acceptance and use of such tools independently and innovatively.