Beyond the Dictionary: Redefining Translation Education with Artificial Intelligence-Assisted App Design and Training

Abstract

This study addresses researchers’ concerns about the effectiveness of integrating AI-assisted app design and training into university-level translation courses. It aims to explore how this integration influences students’ motivation, reflective practices, academic performance, and the quality of language translation. Employing a mixed-methods research approach with a two-group post-test-only design, the study uncovers the transformative influence of integrating an AI-based translation assistant. Purposive sampling techniques were utilised to conduct interventions and focus groups with students majoring in Philology enrolled in the 3 ECTS course in Industry-Specific Translation at Taras Shevchenko National University of Kyiv. The findings from the Independent Samples t-test revealed significant improvements in motivation, reflective practices, academic performance, and translation quality among students in the experimental group (EG) compared to the control group (CG). Thematic analysis using the Speak web-based tool indicated an overall positive sentiment (0.224), highlighting Positive and Very Positive sentiments. The Neutral category suggested a balanced perspective, while the combined Slightly Negative and Negative categories represented less favourable sentiment. This research contributes valuable insights to the discourse on AI in language education by addressing gaps in related literature. We suggest avenues for future research into linguistic nuances and contextual challenges, advocating ongoing exploration to maximise the benefits of these technological interventions. Ultimately, this study provides a comprehensive understanding of the impact of AI integration in language education and underscores the need for continued exploration and adaptation in this area.