Modeling artificial intelligence-enabled teaching continuance intentions in higher educational institutions using the expectation confirmation model (ECM)

The digital era has forced higher educational institutions (HEIs) to use artificial intelligence (AI) to improve their services. However, it is still unexplored how artificial intelligence service agents (AISA) can influence the continued intention of AI-enabled teaching. Therefore, this used the expectation confirmation (EC) model to highlight the influence of AI service agents on the continued intention of AI-enabled teaching in HEIs of the gulf cooperation council (GCC). The data were gathered from 528 students in different universities of GCC countries using AI in teaching. The questionnaire was developed to collect the data, and analysis was done by using SmartPLS 4. The results reported that all ASIA, including anthropomorphism, availability, contact, efficiency, enjoyment and security, significantly influence the perceived usefulness of AI-enabled teaching. Moreover, only availability, contact, enjoyment, and security lead to expectation confirmation of AI-enabled teaching among these agents. Perceived usefulness is associated with confirmation, and it can lead to satisfaction. Moreover, perceived usefulness and satisfaction can significantly influence the continued intention of AI-enabled teaching in HEIs. The study used AISA to examine intention in HEIs, but further studies can be conducted on specific education sectors, private or public. Moreover, the study findings can help the policymakers of HEIs to enhance their service quality by using AISA.