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@article{175156,
        author = {sabahuddin ahmad},
        title = {AI and Human Language: The Quest for Linguistic Competence},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {2202-2219},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175156},
        abstract = {The development of linguistically competent AI models remains a central challenge in natural language processing (NLP) and artificial intelligence (AI). While statistical and deep learning-based models have significantly advanced language modeling, current AI systems still struggle with fundamental aspects of linguistic competence, including syntax, semantics, pragmatics, and discourse understanding. This paper explores the evolution of AI-driven language models, from early rule-based approaches to probabilistic and deep learning methods, highlighting their contributions and limitations. It examines key challenges such as data biases, the syntax-semantics-pragmatics gap, and the difficulty of handling long-range dependencies. Additionally, the study discusses ethical concerns, including AI hallucinations and the lack of model interpretability, which impact the responsible deployment of AI in real-world applications. The paper also explores emerging solutions, such as multimodal AI, embodied learning, and memory-augmented architectures, which aim to bridge the gap between statistical language processing and human-like comprehension. Finally, it underscores the importance of interdisciplinary collaboration between linguists and AI researchers in advancing language models toward deeper linguistic competence. Achieving human-level language understanding will require integrating reasoning, adaptability, and contextual awareness, ensuring that AI systems go beyond pattern recognition to truly grasp the complexities of human communication.},
        keywords = {Artificial Intelligence, Computational Linguistics, Deep Learning, Discourse Understanding, Linguistic Competence, Machine Learning, Multimodal AI, Natural Language Processing, Pragmatics, Semantics, Syntax, Transformer Models},
        month = {April},
        }