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Published on: December 15, 2023
Understanding university teachers' continuance of an AI teaching assistant: an integrated TTF-TAM-ECM model in higher
Zhihan Liu1, Sha Cao1, Jingwei Zhang1
1School of Foreign Studies, University of Science and Technology Liaoning, Anshan, China.
Introduction:
The rapid integration of artificial intelligence (AI) into higher education is reshaping teachers' work, yet limited evidence addresses teachers' post-adoption experiences with AI teaching assistants. This study examines university English teachers' continuance use of the Superstar AI Assistant by integrating the Technology Acceptance Model, Expectation-Confirmation Model, and Task-Technology Fit.
Methods:
Survey data from 248 teachers who used the AI assistant for one semester were collected and analyzed using Structural Equation Modeling with bootstrapped mediation analyses.
Results:
Task-technology fit strongly predicts perceived ease of use, confirmation, satisfaction, and behavioral intention, whereas its direct effects on perceived usefulness and actual use are non-significant. Perceived usefulness, ease of use, and confirmation significantly enhance satisfaction, and behavioral intention is the primary driver of actual use. Multiple significant indirect pathways were identified through mediation analyses.
Discussion:
The study advances post-adoption theory in AI-supported teaching and highlights implications for teacher professional development, AI system design, and institutional digital transformation.
