Linguistic and interactive drivers of chatbot acceptance in EMI higher education: A case study of the Tesla chatbot in Serbia

Abstract

This study investigated international students' perceptions of the Tesla chatbot in English-medium instruction (EMI) courses, with a focus on its usability, motivational impact, perceived learning support, and contribution to English language skill development. The primary objective was to determine whether these factors predicted classroom engagement, perceived learning enhancement, support for integration into teaching, and differences by gender. A quantitative, cross-sectional design was used. Data were collected via a 16-item online questionnaire from 54 international students enrolled in the Business Economics program at Singidunum University, Belgrade. The survey was administered immediately after classroom interaction with the chatbot. Analyses were conducted using IBM SPSS Statistics v29.0.2, applying multiple regression, Pearson correlation, and independent-samples t-test. Results indicated that linguistic clarity significantly predicted perceived learning enhancement, whereas technical usability did not, and engagement was not significantly predicted. Motivation to explore artificial intelligence was positively associated with support for regular classroom use and integration with textbooks. No significant gender differences were observed in perceived learning support. Perceived linguistic benefits, especially classroom interactivity and chatbot understanding of questions, significantly predicted support for integration into EMI courses. These findings suggest that linguistic and interactive quality are central to chatbot acceptance in higher education and support their targeted use in language-focused EMI contexts. Future research should explore longitudinal impacts, discipline-specific adaptations, and the integration of adaptive features, while practitioners can prioritize linguistic accessibility and interactive design to maximize educational benefits.

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