The Role of AI in Documenting Pragmatic Markers and Semantic Structures in Undocumented Pakistani Languages

Abstract

The rapid decline of undocumented languages in Pakistan threatens the disappearance of invaluable cultural, historical, and linguistic knowledge. Despite a rich linguistic landscape, many regional and minority languages remain poorly described, with limited textual resources or scholarly analysis. This study explores how Artificial Intelligence (AI), particularly Natural Language Processing (NLP) and machine learning techniques, can be effectively utilized to document pragmatic markers and semantic structures in these endangered languages. Pragmatic markers—words or phrases that regulate discourse and convey speaker intention—are crucial to understanding how language reflects social structure, cognition, and cultural interaction. Drawing on fieldwork conducted in multilingual regions of Pakistan, the study integrates transformer-based AI models with traditional linguistic methods to identify and categorize pragmatic markers and their functions. The research demonstrates that AI enhances the efficiency and accuracy of linguistic documentation, especially when applied to low-resource languages. However, it also reveals the irreplaceable value of human linguistic expertise and community engagement in ensuring the authenticity, interpretability, and ethical soundness of AI-generated insights. The findings challenge universalist assumptions about pragmatics, emphasizing cultural specificity and variation. By bridging technology and field linguistics, this study offers a new model for interdisciplinary collaboration in language preservation, paving the way for more inclusive, ethical, and effective documentation practices. Recommendations include developing AI models adapted to local linguistic typologies, promoting multimodal and community-led research methodologies, and ensuring equitable access to linguistic resources for marginalized language communities.