Multidimensional determinants of generative AI acceptance in foreign language education

Area of Science:

• Educational Technology
• Artificial Intelligence in Education
• Foreign Language Pedagogy

Background:

• Generative artificial intelligence (GenAI) is transforming education, particularly foreign language (FL) learning.
• Research has focused on GenAI effectiveness, neglecting learner acceptance and usage.
• The Unified Theory of Acceptance and Use of Technology (UTAUT) provides a framework for understanding technology adoption.

Purpose of the Study:

• To investigate the factors influencing Chinese FL learners' acceptance and usage of GenAI.
• To explore the relationships between UTAUT constructs, learner demographics, emotions, AI literacy, and AI self-efficacy.
• To extend the UTAUT model in the context of GenAI adoption in FL education.

Main Methods:

• Survey administered to 409 Chinese FL learners.
• Structural Equation Modeling (SEM) to analyze relationships between variables.
• Moderating effect analysis to examine the influence of demographics (gender, region).

Main Results:

• Performance Expectancy and Social Influence significantly predicted Behavioral Intention.
• Behavioral Intention, emotions, AI literacy, and AI self-efficacy significantly influenced Actual Usage.
• Gender and region moderated specific pathways in the UTAUT model.

Conclusions:

• The study validates and expands the UTAUT model for GenAI adoption in FL learning.
• Findings offer theoretical insights into learner acceptance of emerging educational technologies.
• Practical implications for educators and developers to enhance GenAI integration in FL pedagogy.