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Article published In: Interaction Studies
Vol. 25:3 (2024) ► pp.340–368

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Impact of AI chatbots on EFL learners’ technology adoption

An extension of the UTAUT2 model

ORCiD logo with link | Wuhan Polytechnic University
ORCiD logo with link | University of Hong Kong
Published online: 27 June 2025
DOI logo with link
https://doi.org/10.1075/is.24019.hua
Abstract
This study investigates the factors influencing English as a Foreign Language (EFL) learners’ adoption of AI chatbots by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. The research incorporates two additional constructs, Core Self-Evaluation (CSE) and Learning Value (LV), to enhance the model’s predictive power in the context of language learning technology.
A quantitative approach was employed, collecting data from 362 English-major undergraduates at a prominent university, using structured survey questionnaires. The data were analyzed using partial least squares structural equation modeling (PLS-SEM) to evaluate the relationships within the augmented UTAUT2 model.
The results reveal that performance expectancy, facilitating conditions, habit, CSE, and LV significantly influence EFL learners’ behavioral intentions and actual use of AI chatbots. Effort expectancy, social influence, and hedonic motivation were found to have no significant impact on adoption intentions. These findings underscore the importance of aligning AI chatbot functionalities with learners’ educational goals and supporting their self-evaluative beliefs to promote technology acceptance in language learning.
The study advances the UTAUT2 model by demonstrating the relevance of CSE and LV in predicting EFL learners’ adoption of AI chatbots. The findings offer insights for educators and developers to enhance chatbot design, meeting learners’ pedagogical needs and expectations.
Article outline
  • 1.Introduction
    • 2.1Integration of AI technology in EFL learning
    • 2.2Application and integration of the UTAUT2 model in language learning
      • 2.2.1Student attitudes and acceptance of AI chatbots
      • 2.2.2Integration of CSE and learning value constructs
    • 2.3Interaction authenticity and its role in EFL chatbot adoption
  • 3.Model conceptualization and hypotheses development
    • 3.1Performance expectancy (PE)
    • 3.2Effort Expectancy (EE)
    • 3.3Social Influence (SI)
    • 3.4Facilitating Conditions (FC)
    • 3.5Hedonic Motivation (HM)
    • 3.6Habit (HT)
    • 3.7Learning Value (LV)
    • 3.8Core Self-Evaluation (CSE)
    • 3.9Behavioral Intention (BI) and Use Behavior (USE)
  • 4.Methods
    • 4.1Participants and setting
    • 4.2Research model
    • 4.3Data collection and analysis
  • 5.Results
    • 5.1Common method bias analysis
    • 5.2Measurement model
    • 5.3Structural model and hypotheses testing
  • 6.Discussion
    • 6.1Findings
    • 6.2Enhancing the pedagogical effectiveness of AI chatbots through learner acceptance and engagement
    • 6.3Interaction authenticity, learning value, and intention to use
    • 6.4Implications for educational practice and AI chatbot design
  • 7.Limitations and future research directions
  • 8.Conclusion
  • References
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