Integración de la inteligencia artificial en la enseñanza de las lenguas aymara y quechua: una revisión sistemática
Artificial Intelligence Integration in aymara and quechua language teaching: a systematic reviewBarra lateral del artículo
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Contexto: Las lenguas aymara y quechua, habladas por más de diez millones de personas en América Latina, enfrentan un riesgo inminente de extinción debido a la migración, la globalización y la insuficiente promoción educativa. La inteligencia artificial (IA) ha emergido como una alternativa tecnológica con potencial para contribuir a la preservación y revitalización de estas lenguas originarias. Objetivo: Analizar la evidencia científica disponible sobre la integración de la inteligencia artificial en la enseñanza de las lenguas aymara y quechua, identificando las aplicaciones desarrolladas, los enfoques metodológicos empleados y las limitaciones reportadas en la literatura. Metodología: Se realizó una revisión sistemática siguiendo la declaración PRISMA, con búsqueda en las bases de datos Scopus, Web of Science, ERIC, SciELO y IEEE Xplore, abarcando el periodo 2018-2026. Se incluyeron 50 estudios que cumplieron los criterios de elegibilidad. Resultados: Se identificaron tres categorías principales: aplicaciones de IA para la enseñanza de lenguas indígenas, tecnologías de procesamiento del lenguaje natural para lenguas de bajos recursos, y desafíos éticos y socioculturales en la integración de la IA. Las herramientas más frecuentes incluyen traducción automática, reconocimiento de voz y realidad aumentada. Conclusión: La IA ofrece oportunidades significativas para la preservación lingüística del aymara y el quechua, aunque persisten barreras técnicas, pedagógicas y éticas que requieren atención prioritaria.
Background: Aymara and Quechua, spoken by over ten million people in Latin America, face an imminent risk of extinction due to migration, globalization, and insufficient educational promotion. Artificial intelligence (AI) has emerged as a technological alternative with the potential to contribute to the preservation and revitalization of these native languages. Objective: To analyze available scientific evidence on the integration of artificial intelligence in Aymara and Quechua teaching, identifying developed applications, methodological approaches, and limitations reported in the literature. Methods: A systematic review was performed following the PRISMA statement, searching Scopus, Web of Science, ERIC, SciELO, and IEEE Xplore databases for the 2018-2026 period. Fifty studies meeting eligibility criteria were included. Results: Three main categories were identified: AI applications for teaching indigenous languages, natural language processing technologies for low-resource languages, and ethical and sociocultural challenges in AI integration. Frequent tools include machine translation, voice recognition, and augmented reality. Conclusion: AI offers significant opportunities for linguistic preservation, although technical, pedagogical, and ethical barriers persist that require priority attention.
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