Local Languages AI: Toward Linguistically Inclusive, Cognitively Diverse, and Regenerative Artificial Intelligence

Artificial Intelligence (AI) has become one of the most influential cognitive infrastructures of the twenty-first century. The rapid emergence of large language models (LLMs), foundation models, and multimodal AI systems has transformed how knowledge is produced, communicated, and accessed globally. However, despite these advances, contemporary AI remains profoundly linguistically asymmetric. A limited number of high-resource languages, particularly English, Chinese, Spanish, and French, dominate training datasets, benchmarks, computational resources, and research agendas. As a consequence, thousands of languages spoken across Africa, Latin America, Oceania, and Indigenous communities worldwide remain marginal within the emerging architecture of artificial intelligence. Existing research has primarily approached local and Indigenous languages through the framework of low-resource natural language processing, emphasizing challenges related to corpus scarcity, machine translation, and computational limitations. While these approaches have significantly advanced multilingual AI, they often maintain an underlying assumption that languages are primarily communication systems requiring technological representation. This article challenges this assumption by proposing a fundamentally different paradigm: Local Languages AI, which conceptualizes linguistic diversity not as a technical obstacle but as a strategic cognitive resource for the future development of artificial intelligence. The article introduces the Linguistic Cognitive Infrastructure Theory (LCIT), which argues that languages function as dynamic cognitive infrastructures that organize perception, categorization, memory, ecological knowledge, social intelligence, ethical reasoning, and innovation. From this perspective, the exclusion of local languages from AI development represents not only linguistic inequality but also the exclusion of alternative forms of human cognition from global technological systems. Rather than asking how artificial intelligence can preserve local languages, this article asks how local languages can expand the intelligence, adaptability, and contextual understanding of artificial intelligence itself. Building on LCIT, the article proposes the Local Languages Artificial Intelligence Framework (LLAI Framework) as an analytical model for developing linguistically inclusive and regenerative AI ecosystems. The framework integrates eight interconnected dimensions: linguistic diversity, cultural representation, knowledge preservation, ethical alignment, community participation, computational accessibility, cognitive inclusion, and digital sovereignty. These dimensions move beyond conventional AI evaluation approaches focused primarily on performance and accuracy by incorporating epistemic diversity and human knowledge systems into the design of future AI. The article further identifies potential methodological extensions, including the Cognitive Linguistic Richness Index (CLRI) for assessing the cognitive contribution of languages to AI systems and the Local Languages AI Readiness Index (LLARI) for evaluating institutional capacity to develop linguistically inclusive AI ecosystems. These proposed instruments aim to complement existing AI metrics by incorporating linguistic resources, knowledge sovereignty, community participation, and cultural intelligence. Using African multilingualism as an illustrative context, the article demonstrates how languages such as Swahili, Yoruba, Amharic, Lingala, Kikongo, Tshiluba, and Wolof preserve complex systems of ecological knowledge, social organization, conflict resolution, and adaptive intelligence that remain insufficiently represented in contemporary AI systems. The article argues that the future of trustworthy and regenerative artificial intelligence will depend not only on larger models, greater computational capacity, and expanded datasets, but also on the ability of AI systems to incorporate humanity’s full linguistic and cognitive diversity.