Current Concepts in Engineering Research and Technology Vol. 4

Digital extension has reduced the marginal cost of reaching dispersed smallholder farmers, but access to information is not equivalent to access to usable knowledge. Language, literacy, trust, connectivity, and the institutional organisation of extension determine whether digital advice can be understood, acted upon, challenged, and adapted. This critical narrative review examines how artificial intelligence (AI), multilingual language technologies, voice interfaces, and locally grounded advisory systems are reshaping agricultural knowledge access for smallholder farmers. Literature published from 1 January 2000 to 7 August 2026 was identified through multidisciplinary, agricultural, biomedical, computing, and regional scholarly sources, supplemented by citation searching and bibliographic verification. Evidence from randomised field experiments, observational studies, qualitative research, systematic reviews, meta-analyses, human-computer interaction studies, and multilingual natural-language-processing research was appraised for causal credibility, external validity, language relevance, and equity implications. The evidence indicates that digital advisories can improve knowledge and adoption on average, but agronomic and income effects are modest and heterogeneous, and direct causal evidence for multilingual generative-AI systems remains limited. Local-language and voice delivery can reduce literacy-related barriers, yet low-resource language scarcity, dialect variation, speech-recognition error, translation loss, and agricultural terminology create new failure modes. Retrieval-augmented generation and multimodal interfaces can improve grounding and accessibility, but do not by themselves solve problems of outdated content, unsafe recommendations, unequal device access, weak feedback, or accountability. Democratisation therefore depends less on the number of languages nominally supported than on the architecture of participation: reliable local knowledge bases, transparent provenance, human escalation, farmer feedback, social learning, data rights, and public-interest governance. The strongest near-term model is a hybrid system in which AI expands reach and responsiveness while extension workers, farmer organisations, and local communities retain central roles in interpretation, validation, and knowledge co-production.