Designing an Intelligent Adaptive Learning Environment

Document Type : Qualitative Research Paper

Authors

1 PhD student in Educational Technology, Allameh Tabataba'i University, Tehran, Iran.

2 Associate Professor, Educational Technology Department, Allameh Tabataba'i University, Tehran, Iran.

3 Assistant Professor and Head of the Cognitive Sciences Laboratory, Allameh Tabataba'i University, Tehran, Iran.

10.22034/jsa.2026.143895.2586

Abstract

The rapid advancement of artificial intelligence technologies, traditional learning environments have undergone a fundamental transformation. Concepts such as "intelligent adaptive learning" have emerged as innovative approaches to enhancing educational quality. An intelligent adaptive learning environment is a dynamic, data-driven system that personalizes educational content, difficulty levels, and learning pathways by analyzing learners’ characteristics, needs, and performance. The present study aims to identify and categorize the key components involved in designing intelligent adaptive learning environments through a systematic review of existing research. To achieve this goal, 342 scientific articles published between 2020 and 2025 were collected and analyzed from reputable databases such as Scopus, Web of Science, ERIC, Springer, IEEE Xplore, ScienceDirect, and Google Scholar. Following a rigorous screening process based on inclusion and exclusion criteria, 56 articles were selected for final analysis. Based on qualitative content analysis of the selected studies, the design of an intelligent adaptive learning environment requires consideration of eight key components: learner analysis and assessment, modeling and profiling, application of artificial intelligence technologies, content personalization, design of adaptive learning paths, intelligent feedback and interventions, specialized interactive tools, and continuous monitoring and improvement. Each of these components includes specific subcomponents that collectively enable personalized, data-driven learning. The findings of this study present a structured conceptual framework for the design, implementation, and evaluation of intelligent learning environments, offering a scientific foundation for the development of adaptive educational systems and future research in AI-based learning.

Keywords

References

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History

  • Receive Date: 08 March 2025
  • Revise Date: 28 April 2026
  • Accept Date: 13 June 2026

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