ORIGINAL RESEARCH article

Front. Psychol.

Sec. Educational Psychology

Understanding University Teachers' Continuance of an AI Teaching Assistant: An Integrated TTF–TAM–ECM Model in Higher Education

    ZL

    Zhihan Liu 1

    SC

    Sha Cao 1

    JZ

    Jingwei Zhang 1

    YC

    Yuanyuan Chen 1

    TP

    Thanawan Phongsatha 2

    SP

    Satha Phongsatha 2

  • 1. University of Science and Technology Liaoning, Anshan, China

  • 2. Assumption University, Bangkok, Thailand

The final, formatted version of the article will be published soon.

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 analyzed using Structural Equation Modeling. Results show that task–technology fit strongly predicts perceived ease of use, confirmation, satisfaction, and behavioural 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 behavioural intention is the primary driver of actual use. Bootstrapped mediation analyses reveal multiple significant indirect pathways. The study advances post-adoption theory in AI-supported teaching and highlights implications for teacher professional development, AI system design, and institutional digital transformation.

Summary

Keywords

continuance intention, expectation-confirmation model, task-technology fit, teacher adoption, Technology acceptance model

Received

11 December 2025

Accepted

20 February 2026

Copyright

© 2026 Liu, Cao, Zhang, Chen, Phongsatha and Phongsatha. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sha Cao

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Outline

Share article

Article metrics