<![CDATA[Background: The need for adaptive methods in English language learning has driven the adoption of Artificial Intelligence (AI) tools like ChatGPT and Gemini AI to overcome the limitations of personalized feedback in conventional methods. Teachers must be equipped to guide students in utilizing AI as a catalyst for inquiry-based learning and creative problem-solving, rather than merely generating content. Aims: Utilizing the UTAUT framework, this research investigates the factors driving AI adoption in English language education. It examines how Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) shape students' Behavioral Intention (BI), while also exploring the roles of both BI and FC in determining students' actual use (AB) of AI tools. Methods: To execute this quantitative framework, a survey was deployed to collect data from 171 junior high school (SMP) students in Jambi City, which was then analyzed using Partial Least Squares Structural Equation Modeling (SEM-PLS). Results: The empirical analysis indicates that the measurement model (outer model) faces significant validity and reliability issues. This is demonstrated by a near-complete failure in discriminant validity, with nine of the ten construct-pair Heterotrait-Monotrait Ratios (HTMT) exceeding 1.00, and low convergent validity for the FC and AB constructs. Due to highly overlapping perceptions among adolescent respondents, the robustness of the resulting structural relationships (inner model) cannot be fully generalized.Conclusion: This study concludes that the standard UTAUT instrument cannot be directly applied to minors. Deep linguistic simplification and contextual adaptation are required to ensure the instrument is relevant and accurately understood by middle school students.]]>
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