Analyzing Translations of Abstracts in Physical Education Theses Based on the MQM:
Human versus AI-Generated Translations
Abstract
The study focuses on analyzing the quality of abstract translations in Physical Education theses. It aimed to identify errors in human versus AI-generated translations. The paper also aimed to explore human-generated translations using the Multidimensional Quality Metrics (MQM) framework. For this purpose, 30 English abstracts from the field of sports physiology, spanning 15 years (1390s and 1400s/ 2011 to 2025), were selected. Errors were categorized and quantified by type and frequency. The analysis revealed a fluctuating pattern, characterized by a significant recent improvement. Results showed a general upward trend in errors from 2011 to 2020, including a gradual decline in abstract quality—a trend potentially linked to an over-reliance on machine translation. This pattern reversed sharply in the 2020s, with data showing a consistent and marked reduction in errors, culminating in a 71% decrease by 1404 (2025) and reflecting a substantial enhancement in translation quality during the early years of the 1400s (2021–2025).
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Copyright (c) 2026 Narjes Asgari Vartooni, Mahmoud Afrouz, Vazgen Minasian
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Copyright Licensee: Iranian Journal of Translation Studies. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0 license).
