Understanding EFL Students’ Acceptance of DeepL for English Translation: A Technology Acceptance Model Perspective
DOI:
https://doi.org/10.24256/ideas.v13i2.6831Keywords:
DeepL, English As Foreign Language, Machine Translation, Technology Acceptance Model, User experienceAbstract
This study investigates University students' perceptions of the machine translation tool DeepL in the context of learning English as Foreign Language (EFL). The main objective was to explore the frequency of use with the tool through the TAM Theory. Using a descriptive qualitative research approach, the purpose of sampling was used to select four university EFL students, who were categorized as frequent and infrequent users of DeepL. Data was collected through semi-structured interviews, and questionnaires. The results showed that frequent users of DeepL appreciated its effectiveness in vocabulary acquisition, translation accuracy, and academic writing support. In contrast, infrequent users expressed concerns about limitations such as the lack of a paraphrasing feature and the formality issues. These findings highlight significant differences in user experience based on frequency of use and the need for further research with larger and more diverse samples to validate these results. Recommendations for future research include incorporating feedback from users to improve functionality and meet the evolving needs of EFL learners. This research contributes to the understanding of the role of machine translation technology in language learning and offers insights for future research.
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