ArticleFrontiers in psychology2026
Understanding university teachers' continuance of an AI teaching assistant: an integrated TTF-TAM-ECM model in higher education.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.