Artificial Intelligence Redefines Personalized Learning in Education

Article: The increasing integration of artificial intelligence (AI) in the field of education is profoundly transforming teaching practices and learning experiences. A recent systematic study, covering the period from 2019 to 2025, highlights the evolution of concepts and terminologies related to personalized learning, a rapidly changing field thanks to technological advancements. Personalized learning, a learner-centered approach, aims to adapt teaching to individual needs, goals, abilities, motivations, and interests. However, the rise of digital technologies and AI has introduced terminological complexity, where terms such as adaptive learning, individualized instruction, and tailored learning are often used interchangeably, creating conceptual ambiguity.

The study reveals that AI now enables dynamic learning pathways based on real-time data, instant feedback, and continuous adjustments to content and pace. Adaptive learning systems, intelligent tutors, and data analytics tools play a key role in this transformation, making education more inclusive, engaging, and effective. Emerging technologies, such as generative AI or personalized recommendation systems, expand access to quality education and promote educational equity. Yet, despite these advances, major challenges persist, particularly in data protection, algorithmic bias, teacher preparation, and human-machine collaboration.

One of the main findings of this research is the need to clarify the definitions and practices associated with personalized learning. The authors emphasize that current theoretical frameworks struggle to keep pace with technological innovations, leaving critical questions unanswered. For example, ethical concerns, such as data privacy or algorithmic transparency, remain insufficiently addressed. Additionally, research often lacks methodological rigor, especially in diverse educational contexts or among underrepresented populations.

The study distinguishes several key concepts: adaptive learning, which relies on algorithms to automatically adjust difficulty, pace, and content based on learner performance; individualized instruction, which manually tailors the educational path according to skills and interests; and tailored learning, which uses technological tools to personalize resources and interfaces based on learner preferences. Intelligent tutoring systems, meanwhile, represent an advanced technological application of personalized learning, providing individualized support through sophisticated learning models and immediate feedback.

The results show that the effectiveness of personalized learning largely depends on the disciplinary context and the learner profile. For example, computer science students develop more effective learning strategies over time, while those in mathematics may require more structured guidance to avoid avoidance behaviors. This underscores the importance of a context-sensitive approach, where teaching strategies are aligned with the specific needs of learners.

Another major challenge is the complementary role of teachers and AI systems. Teachers remain essential for ensuring pedagogical coherence, ethics, and socio-emotional support, while AI brings increased precision and scalability through data analysis and real-time adaptation. Together, they form an indispensable partnership to maximize the benefits of personalized learning.

The study proposes a unified definition of personalized learning in an AI-enriched context, integrating eight essential components: learner profile, input data, content model, instructional model, adaptive engine, user interface, personalization goals, and evaluation and governance. These elements highlight that personalized learning, in a technology-rich environment, must be considered a socio-technical system combining pedagogical design, algorithmic processes, and ethical governance.

Finally, the authors call for strengthened collaboration between AI developers and educators to design pedagogically robust, accessible, and adaptable tools to meet the evolving needs of education. They also emphasize the need to conduct systematic evaluations of personalized learning platforms to measure their effectiveness across different learner profiles and educational objectives. In the era of AI, personalized learning offers unprecedented opportunities to transform education, provided that the conceptual, technical, and ethical obstacles that remain are overcome.

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DOI : https://doi.org/10.1186/s40561-026-00440-6

Titre : Redefining personalized learning in the artificial intelligence era: an updated systematic review from 2019 to 2025

Revue : Smart Learning Environments

Éditeur : Springer Science and Business Media LLC

Auteurs : Fatima Khalifeh; Raúl Santiago; Ramon Palau

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