Acceptability, Adoption, and Policy Directions for Artificial Intelligence in Language Learning
Abstract
This study investigated the acceptability and adoption of Artificial Intelligence (AI) in fostering autonomous language learning. Utilizing a quantitative cross-sectional design, the researcher surveyed 575 participants, comprising 550 students and 25 language instructors. The research grounded its constructs in the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The analysis revealed a high degree of systemic acceptability among both cohorts. Perceived usefulness emerged as the primary driver of adoption, serving as the strongest predictor of behavioral intention (β= 0.34, p < 0.001). Furthermore, trust and reliability significantly influenced the participants' willingness to integrate these tools into their learning routines. Overall, the structural model accounted for 62% of the variance in behavioral intention. These findings demonstrated that while learners and educators are open to AI, successful long-term integration depends on optimizing usability, institutional trust, and equitable access. Consequently, this study developed a comprehensive policy framework designed to guide stakeholders in the responsible and pedagogically sound adoption of AI. This roadmap addressed critical concerns regarding ethical use and the mitigation of "cognitive laziness," ensuring that AI serves as a catalyst for – rather than a replacement of – genuine linguistic acquisition.
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