ArticleScientific reports2026
Multidimensional determinants of generative AI acceptance in foreign language education.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- The Impact of Generative Artificial Intelligence Use on Perceived English Learning Achievement: The Roles of Use Behavior and Task-Technology Fit.Behavioral sciences (Basel, Switzerland) · 2026Article
- Determinants of off-label drug acceptance in pregnancy: insights from an extended UTAUT model.Frontiers in pharmacology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Digital technology has ignited new expectations for educational transformations, with generative artificial intelligence (GenAI) at the forefront. As GenAI-driven technologies have permeated multiple aspects of foreign language (FL) pedagogy, research has predominantly probed its effectiveness while paying little attention to the learners' acceptance and usage. The present study delves into this issue. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) model, the study explores the interconnection between UTAUT constructs (i.e. Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Behavioral Intention, Actual Usage) and potential variables, namely participants' demographics, emotions, AI literacy and AI self-efficacy. Collecting a total of 409 valid replies of Chinese FL learners from various universities, the study employed Structural Equation Modeling and moderating effect analysis to examine the hypothetical relationships. Results indicate that Performance Expectancy and Social Influence emerge as significant predictors of Behavioral Intention, and Actual Usage is significantly influenced by Behavioral Intention, emotions, AI literacy and AI self-efficacy. Additionally, gender and region demonstrate a moderating effect on specific paths within the overall model. These findings validate and expand the UTAUT model into a bigger picture, enhancing the understanding of FL students' acceptance of GenAI technology from both theoretical and practical perspectives.
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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.