Main content
1
AI and Personalized Learning
2
Learning Data – A Personalization Platform
3
The effectiveness of AI-driven learning
4
Challenge: Data & Equity
5
Vietnam's Perspective
1. AI is driving the shift from “one-size-fits-all” to Personalized Learning.
In traditional education, most learners receive the same content, at the same pace, and are assessed in the same way, regardless of their individual abilities and needs. AI is gradually changing this approach.
International studies show that, by analyzing learning behavior data, AI can tailor learning content, difficulty, and feedback to each individual. Instead of following a common path, each learner is guided along a personalized learning journey.
Analysis of over 100 studies across various educational contexts also indicates that AI plays a central role in building adaptive learning systems, from primary and secondary education to higher education and online learning.
(Source: Springer, 2025)
2. Learning Data – the foundation for creating personalized learning paths.
In the age of AI, personalized learning is inseparable from learning data. Learning data is generated from learners' daily interactions on digital platforms, such as study time, progress, common mistakes, engagement levels, and progress over time.
Thanks to this data, AI can better understand how each person learns, rather than making decisions based on general averages. Studies show that learning data helps systems recommend content, adjust learning paths, and provide learning feedback based on each individual's actual learning status.
In this context, learning data is not only the "fuel" for AI systems, but also becomes a picture reflecting the abilities and development of learners over time.
(Source: Vieriu & Petrea, 2025)
3. Empirical evidence on the effectiveness of AI-driven Personalized Learning
Numerous studies have shown that personalized learning using AI yields significant results. A pilot study in American higher education found that students using AI learning assistants 24/7 experienced a 20% increase in GPA, a 13% increase in final exam scores, and a 36% increase in motivation compared to the group not using AI.
In general education, a meta-analysis of 32 K-12 STEM education trials also noted that the application of personalized AI significantly improved learning outcomes compared to traditional methods.
These results show that AI not only supports but also enhances learning when implemented correctly.
(Source: Springer, 2025)
4. Challenges related to data, privacy, and equitable access.
Alongside its benefits, AI in education also raises several concerns. Learners and parents appreciate AI as a supportive tool, but the biggest concern is how learning data is collected and used.
Several surveys indicate that nearly 70% of K-12 parents oppose sharing student data with AI systems due to privacy and security concerns. Simultaneously, disparities in technological access create challenges regarding equity in personalized learning.
This shows that learning data is only truly valuable when it is managed transparently and placed under the control of the learner.
(Source: Balaban, 2024 – Forbes)
5. The Vietnamese Perspective: Personalized Learning in Digital Education
In Vietnam, personalized learning is clearly developing alongside the digital transformation of education. Thanks to AI, learning content and pathways can be flexibly tailored to the abilities and needs of each learner, particularly effective in training skills and professional competencies. Simultaneously, analyzing learning data helps detect potential disruptions early, allowing for timely adjustments to learning pathways and promoting a learner-centered training model – a crucial direction for digital education in Vietnam.
(Source: Informing Science, 2025)
The RETUDY team's perspective
We view personalized learning as an inevitable development in the AI era, where AI acts as a learning assistant, tailoring learning paths to individual abilities and goals. RETUDY uses learning data transparently, with oversight, and for the benefit of the learner. The focus is not on technology, but on the learning experience and genuine progress. RETUDY aims for flexible, humane learning pathways suitable for the digital education landscape in Vietnam.
AI and personalized learning are gradually reshaping how education is designed and implemented, shifting from a mass learning model to a personalized learning path. As learning data becomes the foundation, the learning process is continuously adjusted based on the learner's abilities, progress, and goals. However, personalization is only truly sustainable when learning data is managed transparently, securely, and under the learner's control.
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