AI-Supported E-Learning Systems and Continuance Intention: Extending the ECM Model in the Context of Education, Communication, and Management

Abstract

Artificial intelligence (AI) has proven to be a disruptive educational technology in the e-learning system environment, offering significant practical value to both learners and educational institutions. AI plays a critical role in facilitating users' acceptance and adoption of e-learning systems. However, the way AI characteristics influence users' continuance intention to use AI-supported e-learning systems from the perspective of the Expectation-Confirmation Model (ECM) has not been thoroughly studied. To address this research gap, this paper develops a research model that incorporates three constructs related to AI characteristics: perceived intelligence, perceived anthropomorphism, and perceived personalization. The model explores users' continuance intention within this context using the ECM framework. A survey method was employed, and 425 valid responses were collected through random sampling. The model was tested using Partial Least Squares (PLS). The results indicate that intelligence, anthropomorphism, and personalization not only directly influence users' perceived usefulness and satisfaction, thereby promoting their intention to continue engaging with e-learning systems, but also enhance user satisfaction through confirmation and perceived usefulness, further encouraging continued use. This paper contributes to theoretical advancements, discusses future directions for e-learning system research, and offers practical guidance for application developers on designing and developing suitable e-learning systems using AI technology.