Non English Major EFL Learners’ Perceptions of DeepL Use in a General English Course
DOI:
https://doi.org/10.64733/journalalfa.v4i2s.230Keywords:
DeepL, EFL Learners , General English Course, Information Systems Students , Machine Translation , Students’ PerceptionsAbstract
The rapid adoption of AI-based machine translation tools such as DeepL in English as a Foreign Language (EFL) learning has outpaced research on how non-English-major students perceive and use them, particularly in General English courses rather than translation-specific ones. This gap is significant, as such learners' attitudes and motives shape how they adopt and critically engage with AI-assisted tools. This study aimed to examine Information Systems students' perceptions of using DeepL in a General English course and to identify the factors influencing these perceptions. A descriptive qualitative design was employed, combining questionnaire and interview data through methodological triangulation. Participants were 14 second-semester Information Systems students at Universitas Labuhanbatu, selected through total purposive sampling. Data were collected using an 18-item five-point Likert questionnaire and a structured interview guide. Findings showed predominantly positive perceptions: five statements were rated "Strongly Agree," five "Agree," three "Neutral," and five negatively worded statements were interpreted as positive when Disagreed with. Interviews revealed that these perceptions were driven by positive attitudes and learning motives, time efficiency under assignment deadlines, and DeepL's perceived accuracy, naturalness, and accessibility relative to other translation tools. These findings suggest that instructors should design tasks promoting critical, reflective use of DeepL rather than prohibiting it, to maximize learning benefits while mitigating overreliance.
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