A Systematic Literature Review on Natural Language Processing (NLP)

Castanha, Jick; Indrawati, .; Pillai, Subhash Kizhakanveatil Bhaskaran; Ramantoko, Gadang; Widarmanti, Tri

doi:10.1109/icacnis57039.2022.10055568

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…As demonstrated by recent literature, AI-enabled solutions can play a pivotal role in the automated profiling of individual learners (Driver 1) by applying predictive analytics and machine learning algorithms to student interaction data (Tapalova and Zhiyenbayeva, 2022). The rich insights uncovered on knowledge levels, interests, and evolving needs can inform adaptive content sequencing and recommendation engines (Driver 2) to provide customized learning paths, intelligent tutoring, and conversational learning experiences (Castanha et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Harnessing AI for Education 4.0: Drivers of Personalized Learning

Barrera Castro,

Chiappe,

Becerra Rodriguez

et al. 2024

EJEL

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Personalized learning, a pedagogical approach tailored to individual needs and capacities, has garnered considerable attention in the era of artificial intelligence (AI) and the fourth industrial revolution. This systematic literature review aims to identify key drivers of personalized learning and critically assess the role of AI in reinforcing these drivers. Following PRISMA guidelines, a thorough search was conducted across major peer-reviewed journal databases, resulting in the inclusion of 102 relevant studies published between 2013 and 2022. A combination of qualitative and quantitative analyses, employing categorization and frequency analysis techniques, was performed to discern patterns and insights from the literature. The findings of this review highlight several critical drivers that contribute to the effectiveness of personalized learning, both from a broad view of education and in the specific context of e-learning. Firstly, recognizing and accounting for individual student characteristics is foundational to tailoring educational experiences. Secondly, personalizing content delivery and instructional methods ensures that learning materials resonate with learners' preferences and aptitudes. Thirdly, customizing assessment and feedback mechanisms enables educators to provide timely and relevant guidance to learners. Additionally, tailoring user interfaces and learning environments fosters engagement and accessibility, catering to diverse learning styles and needs. Moreover, the integration of AI presents significant opportunities to enhance personalized learning. AI-driven solutions offer capabilities such as automated learner profiling, adaptive content recommendation, real-time assessment, and the development of intelligent user interfaces, thereby augmenting the personalization of learning experiences. However, the successful adoption of AI in personalized learning requires addressing various challenges, including the need to develop educators' competencies, refine theoretical frameworks, and navigate ethical considerations surrounding data privacy and bias. By providing a comprehensive understanding of the drivers and implications of AI-driven personalized learning, this review offers valuable insights for educators, researchers, and policymakers in the Education 4.0 era. Leveraging the transformative potential of AI while upholding robust pedagogical principles, personalized learning holds the promise of unlocking tailored educational experiences that maximize individual potential and relevance in the digital economy.

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Section: Discussionmentioning

confidence: 99%