Smart learning in programming: a systematic literature review

Authors

  • Edison Amador Miguez Gordillo Universidad Nacional de Chimborazo, Facultad de Ciencias de la Educación, Humanas y Tecnologías, Riobamba, Chimborazo, Ecuador ORCID iD https://orcid.org/0009-0007-3676-2989
  • Patricio Ricardo Humanante Ramos Universidad Nacional de Chimborazo, Facultad de Ciencias de la Educación, Humanas y Tecnologías, Riobamba, Chimborazo, Ecuador ORCID iD https://orcid.org/0000-0003-2632-2051

DOI:

https://doi.org/10.33936/isrtic.v10i1.8443

Keywords:

smart learning, programming, adaptive learning

Abstract

Smart learning in programming education has experienced sustained growth due to the incorporation of artificial intelligence, adaptive systems, and generative tools aimed to personalizing learning. The objective of this study was to analyze the advances, approaches, tools, and challenges associated with the application of intelligent technologies in programming education contexts. To this end, a systematic literature review (SLR) was conducted based on the methodology proposed by Kitchenham, structured into planning, development, and documentation phases. Information was retrieved from the Scopus and IEEE Xplore databases, applying inclusion and exclusion criteria, as well as methodological quality assessment, to select scientific studies published between 2022 and 2026.The results show a predominance of Intelligent Tutoring System (ITS), models based on large language models (LLMs), adaptive platforms, and immersive gamified environments. These technologies enable immediate feedback, adaptation to student performance, and personalized support throughout the learning process. The studies report improvements in academic performance, conceptual understanding, problem solving, code debugging, motivation, and computational thinking as well. The main limitations identified are the excessive dependence on generative tools, the possibility of inaccurate responses, and the limited longitudinal evidence on their effects across different educational contexts. The integration of artificial intelligence and teacher mediation fosters more flexible and autonomous learning experiences centered on the individual needs of programming students.

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Published

2026-06-04

Issue

Vol. 10 No. 1: January-June (2026)

Section

Regular Papers

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Copyright (c) 2026 Edison Amador Miguez Gordillo, Patricio Ricardo Humanante Ramos

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