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Cited by 156Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningOnline Learning and Analytics

Crafting personalized learning paths with AI for lifelong learning: a systematic literature review

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Bayly-Castaneda, K., Ramirez-Montoya, M-S., Morita-Alexander, A.·2024·Frontiers in Education
DOI 10.3389/feduc.2024.1424386
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Abstract

The rapid evolution of knowledge requires constantly acquiring and updating skills, making lifelong learning crucial. Despite decades of artificial intelligence, recent advances promote new solutions to personalize learning in this context. The purpose of this article is to explore the current state of research on the development of artificial intelligence-mediated solutions for the design of personalized learning paths. To achieve this, a systematic literature review (SRL) of 78 articles published between 2019 and 2024 from the Scopus and Web or Science databases was conducted, answering seven questions grouped into three themes: characteristics of the published research, context of the research, and type of solution analyzed. This study identified that: (a) the greatest production of scientific research on the topic is developed in China, India and the United States, (b) the focus is mainly directed towards the educational context at the higher education level with areas of opportunity for application in the work context, and (c) the development of adaptive learning technologies predominates; however, there is a growing interest in the application of generative language models. This article contributes to the growing interest and literature related to personalized learning under artificial intelligence mediated solutions that will serve as a basis for academic institutions and organizations to design programs under this model.

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  • Bayly-Castañeda (2024) profundizó en marcos metodológicos para el diseño de entornos adaptativos, enfatizando el rol de los algoritmos de procesamiento del lenguaje natural en la creación de rutas de aprendizaje dinámicas.

    “Bayly-Castañeda (2024) profundizó en marcos metodológicos para el diseño de entornos adaptativos, enfatizando el rol de los algoritmos de procesamiento del lenguaje natural en la creación de rutas de aprendizaje dinámicas [6].”

    From Avances, Retos Éticos y Perspectivas de la Inteligencia Artificial (IA) en la Educación de América Latina (2025) · cites this paper as [6]

    The Introduction (page unavailable) links generative language models to the design of learning trajectories, and the Personalized learning section discusses adaptive paths and dynamic environments. The paper does not, however, emphasize natural-language-processing algorithms as the means of creating those paths or develop methodological frameworks for designing adaptive environments; its Data extraction and synthesis section describes a classification used for the literature review. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • La IA ha demostrado un potencial significativo para fortalecer la inclusión educativa, especialmente en poblaciones tradicionalmente marginadas por barreras físicas o lingüísticas.

    “Por otra parte, la IA ha demostrado un potencial significativo para fortalecer la inclusión educativa, especialmente en poblaciones tradicionalmente marginadas por barreras físicas o lingüísticas.”

    From Avances, Retos Éticos y Perspectivas de la Inteligencia Artificial (IA) en la Educación de América Latina (2025) · cites this paper as [6]

    The Conclusion describes AI’s potential to provide personalized learning and identifies equitable access as a goal. It does not show that AI has already strengthened educational inclusion, particularly for populations facing physical or linguistic barriers. The Introduction also cautions that unequal access to AI may leave some students behind. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Tecnologías como el reconocimiento de voz, la traducción automática y la generación de subtítulos en tiempo real han mejorado el acceso al contenido para estudiantes con discapacidades auditivas o visuales, así como para hablantes de lenguas indígenas o extranjeras.

    “Tecnologías como el reconocimiento de voz, la traducción automática y la generación de subtítulos en tiempo real han mejorado el acceso al contenido para estudiantes con discapacidades auditivas o visuales, así como para hablantes de lenguas indígenas o extranjeras [4, 6].”

    From Avances, Retos Éticos y Perspectivas de la Inteligencia Artificial (IA) en la Educación de América Latina (2025) · cites this paper as [6]

    The Introduction and Conclusion discuss AI-personalized learning and differing learner needs, but do not report improved access through speech recognition, automatic translation, or real-time captions. Searches for accessibility, disabilities, subtitles, and Indigenous languages found no passage addressing the claimed benefits for the specified students. Page numbers are unavailable in our copy. (LLM, conf=0.72)

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Cites

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Cited by

Sorted by citations

Authors
Bayly-Castaneda, K., Ramirez-Montoya, M.-S., & Morita-Alexander, A.
Affiliations
Tecnológico de Monterrey (MX)
Corresponding author
Ramirez-Montoya, M-S.
Venue
Frontiers in Education
Publisher
Frontiers Media SA
Published
2024-08-08
Volume
9
ISSN
2504-284X (ISSN-L)
DOI
10.3389/feduc.2024.1424386
OpenAlex
W4401434014
Cited by
156
References
4
Citations by year
2026: 75 · 2025: 71 · 2024: 10
FWCI
9.03
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Education (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords
personalized learning paths, lifelong learning, artificial intelligence in education, adaptive learning systems, generative language models, higher education, work-based learning, skill acquisition

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