Coding, Artificial Intelligence in the Context of Deep Learning

The Ministry of Primary and Secondary Education has launched two important national programme agendas almost simultaneously: Deep Learning (PM) and Coding and Artificial Intelligence (KKA). In many schools, both are subsequently understood as two equivalent programmes. Teachers attend PM training on one occasion and KKA training on another. Schools arrange programmes, appoint persons in charge, and then report them through different channels. Both run concurrently, yet as if they are unacquainted with one another. This is a misconception that must be immediately rectified. PM and KKA indeed both support educational transformation, but their conceptual positions differ. KKA answers the question of what students learn in facing the digital world. PM primarily answers how the learning process should take place so that students’ knowledge, skills, character, and responsibility develop holistically. Therefore, PM is neither a competitor nor a companion programme to KKA. PM is the pedagogical umbrella that should properly shelter KKA learning.

In terms of policy, KKA is developed as an elective subject, an extracurricular activity, from Grade 5 of primary school to Grade 12 of senior high school, or as material that can be integrated with other subjects according to the school’s readiness. Meanwhile, PM is not an additional lesson hour nor a separate new subject. PM operates within the learning process of any subject at school. This distinction is important for teachers and principals to know because an error in determining the position will give rise to implementation errors: schools become busy running two programme labels, but do not change the quality of the student learning experience.

KKA is directed at developing computational thinking skills, data analysis, algorithms, programming, digital literacy, and artificial intelligence ethics. Its learning is also not solely intended for mastering technology, but for building critical thinking skills and contextual problem-solving. However, this goal will not be achieved merely by providing computers, introducing applications, or teaching programming languages. Students might be able to write code that runs, but not necessarily understand the problem being solved, the reason for choosing the solution, the benefit of their product, or its social and ethical risks.

Without PM, KKA learning easily falls into technocentrism: technology is placed as the goal, not a tool to humanise, honour, and empower students with dignity. Success is then measured by the functioning of an application, the sophistication of a product, or the number of commands that can be given to a machine. Yet, a functioning digital product is not necessarily beneficial; an efficient algorithm is not necessarily fair; and an answer generated by artificial intelligence is not necessarily correct. Schools could produce technologically adept users who are intellectually passive and morally weak.

PM prevents this reduction through the principles of mindfulness, meaningfulness, and joyfulness. Mindfulness means students know their learning objectives, understand their thinking process, and are aware of the responsibility of using digital technology. In learning artificial intelligence, students should not only ask, ‘How does the machine produce an answer?’ They also need to ask, ‘Where does the data come from, can the answer be trusted, who might be harmed, and what decisions should not be handed over to a machine?’ Meaningfulness means KKA concepts are connected to real problems. Students can use computational thinking to design school waste management, map library needs, help promote local products, or identify patterns of false information. Thus, coding does not stop as a syntax exercise. It becomes a tool for understanding reality, developing creativity, and producing useful solutions. Joyfulness does not mean learning must be filled with games or that the teacher must constantly joke. Joy arises when students feel safe to try, dare to make mistakes, receive reasonable challenges, collaborate, and find solutions after experiencing failure. A code error is not material to embarrass a student, but a gateway for reasoning, feedback, and improvement.

The PM learning experience—understanding, applying, and reflecting—must also be visible in KKA. Students understand concepts and logic, apply them to solve problems, and then reflect on the process, the quality of the results, data security, privacy, algorithmic bias, copyright, and social impact. Assessment must not only check whether a programme can run. Teachers need to assess students’ argumentation, problem-solving strategies, collaboration, creativity, ability to correct errors, and ethical considerations.

Principals, therefore, must not manage PM and KKA as two separate administrative projects. Teacher training, academic supervision, learning communities, and the provision of facilities must bring together technical competence with pedagogical competence. The question is not only whether KKA has been taught, but whether its learning makes students think more deeply, act more responsibly, and be able to reflect on the use of technology. KKA without PM risks becoming shallow technical training. PM without an actual space like KKA may lose the opportunity to confront students with future problems. Both must be brought together appropriately: KKA as the field being studied, PM as the approach guiding the process of studying it. With this relationship, schools do not merely produce students who are proficient in using technology, but also develop human beings capable of controlling technology with awareness and responsibility.