Is education the foundation for mastering artificial intelligence?

Technological boost and global heat

In November 2022, ChatGPT emerged and reached 100 million users in just a few weeks – a record-breaking rate of spread in internet history. By the end of 2023, according to the OECD's AI Incident Monitor, the number of AI incidents reported in the international press had surged by 1,278% year-on-year, coinciding with the explosion of generative models. From content creation and programming to healthcare and public administration, AI is quietly redefining human roles: repetitive tasks are being automated, while the need for supervisory, creative, and collaborative skills with machines is increasing.

This explosion has forced international organizations to urgently define the "rules of the game." On September 7, 2023, UNESCO published the first global guidelines on AI in education with a clear message: technology must be placed within a humane framework and respect human rights, rather than pursuing pure efficiency. Less than six months later, on March 13, 2024, the European Parliament officially adopted the AI ​​Act – the world's first legally binding law on artificial intelligence, classifying education, healthcare, and employment as "high-risk" and requiring all systems to be transparent and have human oversight at every stage. These milestones show that not knowing how to use AI has become a new disadvantage when competing in the digital age.

When society encounters AI: 3 major bottlenecks

While its potential is undeniable, AI presents many intertwined challenges. Firstly, the knowledge and digital skills gap leads many to view AI as a "magic black box." Without the ability to ask the right questions or verify data sources, users are more likely to accept machine-generated results, contributing to the spread of misinformation.

Secondly, the massive data base that AI absorbs is already laden with historical biases. Without ethical and legal capacity, we face copyright infringement, privacy violations, or large-scale emotional manipulation – risks that the AI ​​Act has warned against.

Ultimately, the structure of employment is transforming. AI eliminates the need for many repetitive positions while giving rise to new professions such as prompt engineers, model testers, or ethics supervisors. The skills gap will widen if the education system does not adapt in time.

Education is the "key".

The common thread among these three bottlenecks is the need for universal AI literacy. UNESCO recommends that every educational program, from primary to university, should teach learners "how AI learns," raise awareness of data rights, and teach how to use the tools safely and ethically. Recently, the OECD announced plans to include the "Media and AI Understanding" (MAIL) assessment in the PISA 2029 survey; this promises to create a global benchmark for the level of AI proficiency among 15-year-old students.

In that picture, education plays a three-tiered role. At the basic level, the school's task is to shift from "teaching how to use computers" to "teaching collaboration with AI": understanding the mechanisms of machine learning, knowing how to issue precise commands (prompts), and knowing how to evaluate and adjust the output.

At the advanced level, the program needs to incorporate real-world ethical scenarios – from chatbots writing essays for students to deepfakes – to help learners practice analysis and responsible decision-making.

At the lifelong level, a network of micro-credential courses and certifications helps adult workers continuously update their skills, especially in competencies that are difficult to automate, such as creativity, critical thinking, and leadership in multicultural teams.

Of course, content reform alone is not enough. Teachers must be equipped to become "learning architects," leveraging AI as a teaching aid while maintaining their pedagogical autonomy. At the same time, e-learning databases—recording how students interact with AI—need to adhere to the "right to interpretation" principle as in the AI ​​Act, ensuring transparency and privacy.

From policy to the classroom: A model for action.

At the national level, an AI Competency Strategy by 2030 could set targets such as: 80% of the workforce completing basic AI courses; 100% of teachers having access to UNESCO-standard materials; and all educational institutions having mandatory AI ethics programs. This framework would include a legal framework for supervised testing of new technologies, and an education-AI innovation fund to support schools and businesses in collaborative solution development.

At the local level, community AI learning centers will provide equipment, internet access, mentors, and startup consulting programs. This model is particularly useful in rural areas, where the risk of "digital skills gaps" is highest.

At the enterprise level, the "twin-mentor" model—combining a human mentor with an AI assistant—helps employees shorten their uptime cycle. Simultaneously, "sandbox labs" allow for AI testing on real processes but under strict supervision, transforming internal data into a shared learning asset.

The intersection of these three levels is interdisciplinary collaboration. When computer science students are required to collaborate with colleagues in law, psychology, and design on their graduate projects, they are not only writing algorithms but also learning to assess social impact, consider inclusiveness, and sustainability.

Artificial intelligence is moving beyond the realm of "future technology" to become the default infrastructure of life. As this infrastructure permeates every corner of society, our choices are shrinking: either we proactively take control, or we are swept away. Education – with its function of transmitting knowledge, stimulating critical thinking, and nurturing human values ​​– is the dual foundation that ensures humanity does not lose itself in the whirlwind of automation.