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This study adopts a mixed-method approach, combining experimental and action research methodologies to investigate the effectiveness of ChatGPT-assisted Lexical-syntactic Flexibility practice in enhancing the competence and quality of Chinese-to-English consecutive interpreting. A cohort comprising two groups of Chinese-English Conference Interpreting trainees, initially demonstrating equivalent consecutive interpreting competence as determined by a pre-test, participated in a 14-week pedagogical experimental design to contrast the conventional training approach (control group, n = 17) against an approach incorporating ChatGPT-assisted Lexical-syntactic Flexibility practice (experimental group, n = 16). The post-test’s quantitative quality assessment indicates that the experimental group’s interpreting quality was significantly superior to that of the control group across three dimensions: content, language and delivery. Interpreting output analysis reveals that the target language produced by the experimental group exhibited a greater accuracy in conveying propositions with superior clarity, conciseness, efficiency, fluency and idiomatic precision. Trainees’ self-reports and the instructor’s observations confirm that the practice may enhance the richness, flexibility, usability and idiomaticity of lexical and syntactic resources essential for inter-lingual message transfer, boost the information processing competence and improve the quality of the output. The findings of the study may illuminate promising avenues for incorporating artificial intelligence (AI) tools into interpreter and translator training.
Xinchao Lu (Wed,) studied this question.