Artificial Intelligence for Natural Language Processing of Low-Resource Indigenous Languages in the Americas: A Systematic Review of Methods, Applications, and Effectiveness

Despite recent advances in multilingual artificial intelligence, Indigenous languages of the Americas remain underrepresented in natural language processing (NLP) because of limited corpora, annotations, benchmarks, and reusable resources. This systematic review examines AI-based NLP research on low-resource Indigenous languages published within the 2022–2026 review window. Following PRISMA 2020 guidance and an expanded source-verification and supplementary-search process, the final analytical corpus comprised 283 unique records. Machine translation was the most represented application domain (58.3%), followed by language documentation (51.2%), Indigenous-language education (30.4%), and speech technologies (28.6%). Transformer-related architectures (54.1%) and BERT-based models (34.6%) showed the greatest literature visibility, although verified implementation/evaluation was substantially lower than terminology-level occurrence. Quechua (39.2%) and Guarani (33.6%) were the most represented languages. Multilingual training and fine-tuning were the most frequently identified adaptation strategies, while Accuracy, BLEU, and chrF predominated among evaluation metrics. Overall, the evidence reveals a developing but structurally uneven and methodologically heterogeneous field. Persistent challenges include data scarcity, linguistic complexity, orthographic and dialectal variation, limited benchmark availability, uneven community participation, and underreported Indigenous data-governance practices.