IA generativa en el flujo de postproducción: revisión sistemática de integración en assets, rotoscopía, color y audio
Generative AI in the post-production workflow: a systematic review of integration in assets, rotoscoping, color, and audioBarra lateral del artículo
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Introducción: La inteligencia artificial (IA) generativa está transformando el flujo de postproducción al integrarse directamente en las herramientas que los editores ya utilizan. Objetivo: Sintetizar sistemáticamente la literatura 2020-2025 sobre IA generativa en formación en postproducción para fundamentar lineamientos de innovación educativa. Método: El análisis distingue cuatro fases: generación de assets, rotoscopía asistida, corrección de color y postproducción de audio. Su propósito es ofrecer fundamento a lineamientos de innovación educativa para la formación audiovisual superior, incluidas las universidades públicas con recursos restringidos. Bajo el protocolo PRISMA 2020, se rastrearon Scopus, Web of Science, SciELO, RedALyC y Dialnet, con Google Scholar como fuente complementaria. El cribado retuvo 28 estudios con componente formativo explícito. Resultados: Predominan trabajos concentrados desde 2022, de corte exploratorio y cuasiexperimental, con muestras de 12 a 187 participantes e intervenciones de 4 a 16 semanas, que reportan mejoras iniciales en eficiencia operativa, motivación y calidad técnica mejoras supeditadas a la mediación docente y al modo de evaluar. Conclusión: La IA generativa redefine competencias, secuencias y criterios éticos del taller, si bien la evidencia que articula a la vez lo técnico y lo formativo sigue siendo escasa y dispar.
Introduction: Generative artificial intelligence (AI) is transforming the post-production workflow by becoming directly integrated into the tools that editors already use. Objective: To systematically synthesize the 2020-2025 literature on generative AI in post-production education to inform educational innovation guidelines. Method: The analysis distinguishes four phases: asset generation, assisted rotoscoping, color correction, and audio post-production. Its purpose is to provide a foundation for educational innovation guidelines in higher audiovisual education, including public universities with limited resources. Following the PRISMA 2020 protocol, Scopus, Web of Science, SciELO, RedALyC, and Dialnet were searched, with Google Scholar used as a complementary source. The screening process retained 28 studies with an explicit educational component. Results: The literature is predominantly concentrated from 2022 onward and consists mainly of exploratory and quasi-experimental studies, with samples ranging from 12 to 187 participants and interventions lasting 4 to 16 weeks. These studies report initial improvements in operational efficiency, motivation, and technical quality, although such improvements depend on teacher mediation and assessment methods. Conclusion: Generative AI is redefining competencies, workflows, and ethical criteria in audiovisual training, although evidence simultaneously addressing technical and educational dimensions remains limited and heterogeneous.
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