# Understanding university teachers’ continuance of an AI teaching assistant: an integrated TTF–TAM–ECM model in higher education

## TL;DR

This study explores why university teachers continue using an AI teaching assistant, combining theories to understand their experiences and satisfaction.

## Contribution

It introduces an integrated model combining TTF, TAM, and ECM to analyze AI assistant adoption in higher education.

## Key findings

- Task-technology fit strongly influences perceived ease of use and satisfaction.
- Behavioral intention is the main factor driving actual use of the AI assistant.
- Multiple indirect pathways were found linking system perceptions to usage behaviors.

## Abstract

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.

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.

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.

The study advances post-adoption theory in AI-supported teaching and highlights implications for teacher professional development, AI system design, and institutional digital transformation.