AI Insight
This study examined how 22 Chinese interpreting students interact with AI-assisted tools during consecutive interpreting tasks that involve high cognitive load and time pressure. Using eye-tracking, pen-recording, and voice-recording data, researchers identified four distinct interaction patterns: heavy AI users, quick scanners, minimal AI users, and those who frequently switch between AI and traditional note-taking. Students with prior AI training showed more stable interaction patterns, and how students used AI during the comprehension stage, but not the production stage, significantly predicted their interpreting quality.
Why it matters
These findings provide practical insights for designing interpreter training programs and AI-assisted learning tools for high-pressure bilingual tasks. Understanding these interaction patterns can help educators develop targeted training that optimizes student performance when working with AI support systems under time constraints.
Understand the Science
While student-AI interaction patterns have been explored in self-paced cross-linguistic tasks such as L2 writing and translation post-editing, little is known about how students interact with AI in bilingual tasks under intense cognitive and temporal constraints. This study uses computer-assisted consecutive interpreting (CACI) as a window to examine student-AI interaction patterns in such high-stakes environments. Twenty-two Chinese-native interpreting trainees, grouped by prior AI training experience, performed bidirectional CACI tasks using AI-enabled systems integrating automatic speech recognition (ASR) and machine translation (MT) features. With data collected from eye-tracking, pen-recording, and voice-recording, the study reveals that: (a) four interpretable interaction profiles emerge: Intensive Engagers (heavy AI reliance), Fast Scanners (scanning-based processing), Traditionalists (minimal AI reliance), and Frequent Switchers (shifts between AI support and handwritten notes); (b) these patterns shift dynamically between the comprehension and production stages of interpreting, while targeted CACI training was associated with more stable, AI-oriented patterns; and (c) students’ interaction patterns during the comprehension stage, but not the production stage, are significantly associated with performance quality. These findings highlight the heterogeneity and variability of student-AI interaction patterns across different stages of complex bilingual tasks and underscore training effects on shaping students’ behaviors and performance.
Source: Student-AI interaction in computer-assisted consecutive interpreting: patterns and performance