The school system, therefore, is not simply a space for improving learning, but an essential “ground for economic development” where the foundation of technological capabilities is cultivated that will define our countries’ future position and democratize development. Mandatory AI education from age 6 should be a national security and development measure designed to guarantee a constant and massive supply of technological talent.

The education reform plan should last at least 10 years. Within this reform, AI is not conceived as the central core of the scheme, but as a critical instrumental subcomponent for the digitalization strategy.

The specific goals of the reform regarding digitalization should be ambitious: the creation of a digital education system with collaborative services at all levels, the establishment of a national center for educational big data, and the promotion of smart school campuses and advanced digital resources. Explicitly, the plan must “promote AI-driven education reform,” which includes the development of a Large Education Model, the establishment of cloud schools, and the implementation of assessment and decision support systems based on big data and AI.

2. Secondary Education (Ages 12–18)

For Junior High (ages 12–15), the focus is on Logic, Artificial Intelligence (AI), and Critical Thinking.

The program delves into the fundamental principles of AI by teaching machine learning processes, basic data analysis, and the logic that underpins AI systems.

The goal is to develop a stronger technical understanding and foster critical thinking, including the ability to identify misinformation from generative AI.
The Senior High School level (ages 15–18) focuses on applied innovation and systems thinking.

Students engage in more complex, project-based learning. This includes designing and refining AI algorithm models, basic programming, and exploring interdisciplinary AI applications.

The goal is to develop practical innovation skills and the ability to use AI to solve real-world problems.

Participation in high-level competitions (robotics, computer science, mathematical modeling) should be encouraged. This policy indicates that the primary objective of the State is to transcend mere familiarization with AI (usage literacy) and instead create a rigorous technical foundation for development and innovation in data science and engineering.
The deployment of AI in classrooms must be a synergy between the government and a private technology ecosystem, dominated by tech giants like Amazon, Microsoft, and Google.

Technology corporations are infrastructure providers as well as strategic partners in talent development, investing in R&D and fostering an environment of skilled professional competition.

The most direct manifestation of AI in daily teaching is found in adaptive learning platforms, which aim to personalize the student experience.

The mass adoption model for AI introduces significant operational challenges and raises complex questions surrounding governance, ethics, and equity.
The most obvious limiting factor for the program’s scalability is the availability of qualified human capital. The number of experts in new technologies is considered insufficient.

To guarantee the correct implementation of the new curriculum, a training plan is imperative so that teachers, especially those in disciplines such as mathematics, technology, or computer science, acquire the necessary knowledge to teach the new subject. AI requires the teacher’s role to evolve: educators can no longer be mere repeaters of information. Technology will not replace teachers, but it will replace teachers who refuse to learn and use these tools. Therefore, training programs also focus on developing teachers’ skills in using AI tools to create interactive and personalized teaching materials, adapted to the specific needs of each student.

The deployment of AI systems that personalize learning involves the collection and analysis of vast amounts of personal and student performance data. In this context, data privacy and its ethical handling are crucial.

In terms of governance, AI regulation must prioritize national algorithmic security and adherence to the country’s democratic model, unlike models from other continents.

There is active concern about “data contamination” in AI systems, where “falsified, biased, or repetitive” material can be incorporated into training sets, leading to errors in judgment or manipulation of public opinion. To mitigate these risks, we must require companies to “clearly label” AI-generated content. This implies that educational AI must be managed as an information security vector, where control over the veracity and alignment of models is a priority.

AI has the potential to reduce the gap between urban and rural regions by offering personalized resources and intelligent tutoring. However, this promise is challenged by the persistent digital divide, which includes limited access to devices and connectivity in resource-poor environments.

At the academic level, preliminary empirical evidence is positive. Students who have interacted with AI report high acceptance. They feel that AI helps them improve their understanding of the topics, provides them with useful and timely feedback, makes studying more engaging and interesting, and gives them more confidence in their abilities. The desire to continue using AI in the future is consistently high.

Based on the analysis of the educational architecture of our developing countries, the following strategic recommendations are derived for competing or developing educational systems:

• Strategic Review of the STEM Curriculum: It is essential to evaluate the adequacy of current AI and STEM programs. The need to introduce rigorous mathematical training, aligned with data science and AI, at earlier educational stages should be considered.
• Proactive Development of Data Governance: Given the inevitable large-scale deployment of adaptive learning technologies, regulators must establish robust legal frameworks that explicitly address student data privacy and algorithmic ethics, seeking a balance between individual protection and the promotion of technological innovation.
• Massive Investment in Faculty Capital: The success of any educational AI initiative depends on the faculty’s ability to use and guide these tools. It is imperative to launch massive and ongoing teacher training and development programs focused on AI technology and pedagogy to overcome the shortage of experts and ensure educators are prepared for the new role of facilitators.