Artificial Intelligence in Simultaneous Interpreting: Capabilities, Limitations, and Human–Machine Collaboration
DOI:
https://doi.org/10.57125/FS.2026.03.20.10Keywords:
artificial intelligence; simultaneous interpreting, automatic speech recognition, neural machine translation, human–machine collaboration, conference interpreting, cognitive load, computer-assisted interpreting, remote simultaneous interpreting, natural language processing.Abstract
The integration of artificial intelligence (AI) into simultaneous interpreting (SI) constitutes one of the most consequential developments in language mediation studies of the past decade. Recent advances in neural machine translation (NMT), automatic speech recognition (ASR), and large language models (LLMs) have produced systems capable of rendering speech across language boundaries in near-real time, yet the fundamental cognitive, pragmatic, and cultural demands of professional conference interpreting continue to expose critical architectural limitations in current AI designs. This article provides a comprehensive interdisciplinary analysis of the state of AI in SI, structured around three principal aims: (1) to map the cognitive architecture of human SI against the computational strategies of current AI systems; (2) to synthesise empirical evidence on AI interpreting performance across language pairs, registers, and delivery conditions; and (3) to evaluate human–AI collaboration frameworks — including computer-assisted interpreting (CAI) and remote simultaneous interpreting (RSI) platforms — for their practical and ethical implications. We argue that the prevalent framing of AI as a replacement for human interpreters is both technically premature and epistemically inadequate. A more productive paradigm — augmentation — positions AI tools as extending interpreter competencies while preserving the pragmatic, affective, and cultural mediation capacities that remain beyond the reach of existing architectures.
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