AI-ENHANCED PHYSICS INSTRUCTION IN UZBEK: EVALUATING COMPREHENSION OF SCIENTIFIC TERMINOLOGY USING LANGUAGE MODELS

Abstract

This study explores the use of artificial intelligence-based tools, particularly large language models (LLMs), in teaching physics terminology in the Uzbek language. The research focuses on the effectiveness of AI-assisted instruction in improving students’ understanding of core physics concepts expressed in Uzbek. A comparative experimental methodology was employed: one group received traditional instruction, while the other engaged with interactive, AI-supported lessons using localized terminology. The outcomes were evaluated through comprehension tests, semantic accuracy checks, and student feedback. The results indicate that the AI-driven approach significantly enhances learners’ grasp of scientific terms, promotes linguistic clarity, and fosters deeper conceptual understanding in native-language physics education.