Turkish-Language AI Requires More Than a Turkish Interface, Researchers Say

Researchers distinguish between AI systems that merely support Turkish as an interface and those genuinely built around the language's structure. True Turkish-language AI must handle tokenization, training data, morphological complexity, and culturally grounded evaluation benchmarks. Turkish is an agglutinative language, meaning single words can carry multiple grammatical layers, making standard tokenizers less efficient for it than for languages like English. Studies suggest that tokenizer choice and data quality both significantly affect how well a model performs on Turkish tasks. Experts caution that a model's country of origin should not be treated as proof of Turkish-language proficiency without independent benchmark evaluations.