AI-Enhanced Tools in Interpreting Practice: A Systematic Review of Methodological Trends and Empirical Evidence

Abstract

Interpreting is undergoing substantial transformation with the advent of artificial intelligence (AI)-enhanced tools. While empirical research examining their effects has proliferated, review evidence from a methodological perspective remains limited. This systematic review synthesises 53 empirical studies on AI-enhanced tools in interpreting. The analysis shows a marked increase in research since 2023, with a dominant focus on English-Chinese simultaneous interpreting and student interpreters. Most studies examined real-time speech-to-text technologies during interpreting, while pre- and post-interpreting applications received less attention. Although AI support often produced positive or mixed effects on interpreting quality, cognitive load, and user acceptance, these effects varied across tool categories. Methodologically, effect sizes, power analyses, and key moderators such as tool accuracy and latency were inconsistently reported. The findings have important implications for future research design, highlighting the need for greater methodological rigour to support evidence-based human–AI collaboration in interpreting practice.