Understanding AI interpreting in context A comprehension-based evaluation of human vs. machine-generated interpretations in a real-world setting.
Abstract
The rise of AI in the interpreting industry poses pressing questions about the sustainability of interpreting as a profession. While commercial platforms promise real-time multilingual communication at scale, their functional effectiveness in high-stakes professional contexts remains underexplored. This study presents a comprehension-based evaluation comparing human and AI interpreting of a climate-related press conference. Following Reithofer’s (2013, 2014) methodology, 56 journalists were divided into two groups: one listening to professional human interpretation and the other to a cutting-edge AI service (KUDO AI Speech Translator). Results showed that the human group achieved higher comprehension scores (mean 4.5/10) than the AI group (mean 3.7/10), with the latter exhibiting a 17.9% “Don’t Know” rate. Qualitative feedback highlighted that AI’s lack of prosodic salience increased cognitive load, hindering deep information synthesis. These findings suggest that human intervention remains essential for ensuring semantic adequacy and effective information transfer in professional journalistic settings.
Article Details
- How to Cite
-
Matsushita, K. (2026). Understanding AI interpreting in context: A comprehension-based evaluation of human vs. machine-generated interpretations in a real-world setting. International Journal of Language, Translation and Intercultural Communication, 11, 71–85. https://doi.org/10.12681/ijltic.44192
- Section
- Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright Notice
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
