Digital Linguistic Sustainability of Turkish Dialects in AI-Based Language Technologies: Technical Robustness, Representational Justice, and Educational Inclusion

The growing use of artificial intelligence (AI)-based language technologies in learning, assessment, writing support, and speech-enabled educational platforms has made linguistic representation an increasingly important dimension of sustainable and inclusive education. This exploratory mixed-methods study examines how transcribed forms of Turkish dialects are processed within task-appropriate natural language processing (NLP) environments and how their digital representation is interpreted by linguists and dialect speakers. The study combines a spoken-language corpus compiled from 100 speakers in 14 provinces across Türkiye’s seven geographical regions with spaCy- and Stanza-based morphosyntactic outputs, an exploratory BERTurk-based semantic-similarity component, and semi-structured interviews. The corpus contains approximately 60 h of recordings, 120 pages of transcripts, and 3500 annotated structures. Descriptive task-specific observations indicate recurrent processing difficulties involving compound tense forms, non-canonical word order, dialect lexicon, discourse particles, and pragmatically marked expressions. For the POS configurations examined, descriptive POS agreement was higher on standard written Turkish (SWT) reference material (89–91%) than on transcribed dialect data (60–65%). The qualitative material indicates that non-recognition may be interpreted as cultural invisibility, reduced trust in digital tools, and possible pedagogical misalignment. These educational and social dimensions are presented as potential implications rather than directly tested outcomes. The study conceptualizes digital linguistic sustainability through three interrelated dimensions: technical robustness in processing Turkish dialects, the continuity of culturally embedded linguistic knowledge, and educational inclusion.