Department of Engineering, School of Technology and Engineering, National University,
The United States of America
This is an experimental investigation into the efficacy and intuitive comprehension of translating complex, technological topics from English into Farsi (Persian). We pay attention to recent trends in Artificial Intelligence, Artificial General Intelligence (AGI), and Engineering and Technology. Given the significant role of Farsi as the official language of Iran and a widely spoken language across several regions, ensuring accurate and naturally understandable translation of these rapidly evolving technical concepts is paramount for knowledge dissemination. The core of our research investigates how native Farsi speakers intuitively understand the translated materials without additional help. We use state-of-the-art AI tools, including large language models like ChatGPT and Gemini, which excel at generating human-like, conversational text across a diverse range of subjects. These advanced AI systems, trained on vast multilingual datasets, offer promising capabilities for efficient cross-linguistic translation. Our methodology can demonstrate examples of machine translation. We examine various linguistic features, focusing on syntax (sentence structure), semantics (meaning), and pragmatics (contextual relevance and naturalness). By comparing the AI-generated Farsi translations with native speaker’s comprehension, we demonstrate the utility of AI-tools, for technical translation.
Keywords: Artificial General Intelligence (AGI), Semantics, Syntax
The above abstract is a part of the article which was accepted at The 11th International Conference on Languages, Linguistics, Translation and Literature (WWW.TLLL.IR), 1-2 February 2026, Ahwaz.
