This paper investigates how different artificial intelligence systems – namely ChatGPT (GPT-4o), Gemini 1.5 Pro, Qwen, and the Uzbek-language platform Salom-ai.uz – encode temporal concepts when generating Uzbek-language texts. Drawing on a purpose-built elicitation corpus of 24 AI-generated responses to six prompt categories targeting evidentiality, aspect contrast, cyclic time, habitual present, epistemic modality, and conceptual metaphor, this study conducts a systematic discourse analysis using an original seven-category error taxonomy. The findings reveal a hierarchy of temporal fidelity across systems, with Gemini demonstrating the strongest preservation of Uzbek evidential markers, while ChatGPT and Salom-ai.uz exhibit systematic TIME IS MONEY metaphor calquing from English. A paradoxical finding – that the Uzbek-targeted system Salom-ai.uz performed worst on evidentiality retention – points to the insufficiency of the target-language labelling without morphologically informed training. The study contributes an original error taxonomy and the concept of 'evidential erasure' to the emerging field of computational linguistics applied to low-resource agglutinative languages.
| Mualliflar | Marimbaeva, Surayyo |
|---|---|
| Jurnal | Lingvospektr |
| Nashr sanasi | 2026-06-23 |
| Jild | 4 |
| Son | 1 |
| Betlar | 5-11 |
| Til | Ingliz |
Temporal framing, evidentiality, AI-generated text, Uzbek linguistics, discourse analysis, aspect, conceptual metaphor, large language models
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