Integración de IA, pensamiento computacional, reconocimiento de patrones y abstracción en estudiantes de programación: una revisión sistemática

• Juan Pablo Vallejo Bernal ^[1] ; Paula Andrea Rodríguez Marín ^[1]
  1. [1] Instituto Tecnológico Metropolitano

     Instituto Tecnológico Metropolitano

     Colombia

     
• Localización: Comunicar: Revista Científica de Comunicación y Educación, ISSN 1134-3478, ISSN-e 1988-3293, Nº 85, 2026 (Ejemplar dedicado a: Preparar a la Generación Z para las tecnologías emergentes (medios digitales) mediante la formación y la investigación), págs. 155-170
• Idioma: español
• Títulos paralelos:
  • Integration of AI, Computational Thinking, Pattern Recognition, and Abstraction in Programming Students: A Systematic Review
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    Esta revisión sistemática tipo umbrella analiza la convergencia entre pensamiento computacional, abstracción computacional, reconocimiento de patrones e inteligencia artificial en la formación de estudiantes de programación. Se examinaron 50 estudios publicados hasta mayo de 2025 sin restricción del año de publicación, seleccionados de bases de datos como Scopus, Web of Science, IEEE Xplore entre otras, bajo el protocolo PRISMA. El análisis temático permitió identificar patrones recurrentes y vacíos significativos en dicha convergencia. Entre los hallazgos se destaca que, aunque existe abundante literatura sobre cada eje por separado, ningún estudio integra de forma simultánea los cuatro componentes. Además, el 64,7% de las revisiones abordan solo uno de los conceptos, lo cual evidencia una fragmentación conceptual. Esta revisión propone un marco integrador para la enseñanza de la programación que articule estas dimensiones, y plantea líneas futuras de investigación que aborden la creación de marcos pedagógicos, instrumentos de evaluación y validación empírica en contextos formativos.

  • English

    This umbrella systematic review analyzes the convergence between computational thinking, computational abstraction, pattern recognition, and artificial intelligence in the training of programming students. A total of 50 studies published up to May 2025 – without restrictions on the year of publication – were examined, selected from databases such as Scopus, Web of Science, IEEE Xplore, among others, following the PRISMA protocol. The thematic analysis identified recurring patterns and significant gaps in this convergence. Findings reveal that, although there is abundant literature on each axis separately, no study simultaneously integrates all four components. Moreover, 64.7% of the reviews address only one of the concepts, highlighting a conceptual fragmentation. This review proposes an integrative framework for programming education that brings together these dimensions, outlining future research directions focused on the development of pedagogical frameworks, assessment instruments, and empirical validation in educational contexts.

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