September 5, 2025Open Access

Artificial intelligence-powered evaluation model for English translation education in university: Combining quantitative and qualitative methods

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Mixed-methods design shows improved consistency in evaluations among 796 students, suggesting AI can optimize translation education outcomes.

The AI evaluation model improves the consistency of evaluation results and feedback pertinence, enhancing educational outcomes.
Assessment feedback was collected from 796 English-related majors using questionnaire surveys, revealing high trust in AI evaluations.
A mixed-methods design combined quantitative feedback with qualitative insights from focus groups to assess students' views on AI evaluation.
Results indicate a need for balancing AI evaluation with traditional methods, highlighting the importance of teacher involvement in translation education.

Quan et al. (2025) studied this question.

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Mixed-methods design shows improved consistency in evaluations among 796 students, suggesting AI can optimize translation education outcomes.