Empowering Personalized Learning with Generative Artificial Intelligence: Mechanisms, Challenges and Pathways

Abstract

The rapid development of artificial intelligence technology has propelled the automated, humanized, and personalized learning services to become a core topic in the transformation of education. Generative artificial intelligence (GenAI), represented by large language models (LLMs), has provided opportunities for reshaping the methods for setting personalized learning objectives, learning patterns, construction of learning resources, and evaluation systems. However, it still faces significant limitations in understanding the differences in individual static characteristics, dynamic learning processes, and students’ literacy goals, as well as in actively differentiating and adapting to these differences. The study has clarified the technical strategies and application services of GenAI-empowered personalized learning, and analyzed the challenges in areas such as the lag in theoretical foundations and lack of practical guidance, weak autonomy and controllability of key technologies, insufficient understanding of the learning process, lack of mechanisms for enhancing higher-order literacy, and deficiencies in safety and ethical regulations. It has proposed implementation paths around interdisciplinary theoretical innovation, development of LLMs, enhancement of personalized basic services, improvement of higher-order literacy, optimization of long-term evidence-based effects, and establishment of a safety and ethical value regulation system, aiming to promote the realization of safe, efficient, and sustainable personalized learning.

Graphical abstract

Keywords

personalized learning / generative artificial intelligence (GenAI) / learning patterns / application mechanisms

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62037001 and 62337001).

Conflict of Interest

The authors declare that they have no conflict of interest.

Data Availability Statements

The authors confirm that all data generated or analysed during this study are included in this published article.

Authors Contributions

Yaxin Tu contributed to the writing of the original draft, review and editing, as well as conceptualization; Jili Chen participated in the writing of the original draft and was involved in review and editing; Changqin Huang provided contributions to review and editing, along with supervision. All authors whose names appear on the submission made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work; drafted the work or revised it critically for important intellectual content; approved the version to be published; and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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