Computer-assisted interpreting tool and simultaneous interpreters’ perceived cognitive load and adoption intent

Abstract

The rapid integration of Computer-Assisted Interpreting (CAI) tools into professional interpreting practice raises critical questions about their cognitive impact, particularly in Global South contexts where infrastructure and training constraints compound the challenge. This mixed-methods study investigated how the use of InterpretBank, a CAI tool, affects the perceived cognitive load of 18 Indonesian professional simultaneous interpreters during simulated diplomatic interpreting tasks, and what factors drive their intent to adopt the tool. Using a 2×2 within-subjects design (CAI tool use × text difficulty), perceived cognitive load was measured through an 18-item Likert-scale self-rating questionnaire assessing mental effort, stress, frustration, and difficulty of listening, comprehension, and memory, and through retrospective semi-structured verbal protocols probing attention management. Results show that all participants rated their cognitive load as significantly higher in CAI-supported conditions than in non-CAI conditions, with the highest ratings for listening, comprehension, and memory. Qualitative analysis of verbal protocols identified five themes, i.e., attention disruption, memory and listening interference, tool management challenges, strategic coping, and conditional utility. Despite these costs, most participants expressed moderate to high adoption intent, driven by the tool's value for specialist terminology preparation, pre-task glossary building, and post-production accuracy verification. Training was unanimously identified as the primary adoption enabler. These findings suggest the need for locally adapted CAI tools to ensure maximum benefit to the specific demands of a CAI-assisted Simultaneous Interpreting context. Furthermore, the study provides participant-reported evidence establishing a conditional adoption model tailored to professional interpreting in the Global South. Recommendations include tiered CAI training curricula, attention-switching skill development, and the design of offline-capable, locally adaptable tools.