Relative growth momentum, interest across cities, and correlation by grade level.
Indonesian families' interest in coding and artificial intelligence for children strengthened through the first half of 2026, with AI literacy growing fastest. Interest concentrates in digitally ready cities and peaks at upper primary and junior secondary grades. This report maps that momentum through indexed data and official statistics.
Coding and artificial intelligence are moving from the margins of children's learning into the mainstream of what Indonesian families seek. This report captures that momentum in the first half of 2026 through two data lanes: EduPoint's internal analysis presented in indexed form, and official statistics cited directly. Across the observation window, interest in children's coding and AI literacy both strengthened, with artificial intelligence growing the fastest.
This shift unfolds within a supportive policy environment. Through Regulation of the Minister of Primary and Secondary Education Number 13 of 2025, the elective subject Coding and Artificial Intelligence entered the national curriculum for the 2025/2026 school year, rolled out in stages beginning at grades 5, 7, and 10. The policy ties family interest to the formal education agenda, and the internal interest map peaks at precisely the grades that serve as the policy's entry points.
Interest is distributed unevenly across geography. Cities with the highest digital readiness, led by Greater Jakarta, hold the top distribution index, in step with urban internet penetration that exceeds eight in ten residents. Within the skill basket, visual programming serves as a child's first gateway, followed by Python and artificial intelligence literacy, while robotics adds the hardware dimension.
All internal figures appear as indices, ranks, or relative shares to protect confidentiality, while factual claims rest on official sources. This report is a single-period snapshot; a later edition will widen the time range so that seasonality and year-over-year change read more fully.
Coding and artificial intelligence for children are shifting from a hobby of a few families toward a skill seen as worth learning early, driven by curriculum change and the digital economy.
A decade ago, introducing programming to a primary-school child sounded like the ambition of a handful of tech-minded families. By 2026, coding and artificial intelligence occupy a different place: both are viewed as foundational skills worth introducing early, alongside reading, writing, and arithmetic. Interest in children's coding grows together with the awareness that digital skills shape future opportunity.
This report maps interest in children's coding and AI in Indonesia in early 2026. Its focus rests on the direction and structure of interest, set above simple headcounts: how fast interest strengthens, in which cities it concentrates, and at which age level it peaks. The aim is an honest data reference for a wave of interest that has been discussed far more than it has been measured.
The scope covers school-age children's interest in digital skills, from project-based kids coding classes to early AI literacy. Every internal figure appears in indexed form, carrying structure and direction while absolute counts stay withheld. External facts, from curriculum policy to national digital readiness, are cited from official sources and clearly marked. The reading favors stable patterns over passing ripples.
Ministerial Regulation No. 13 of 2025 established Coding and Artificial Intelligence as an elective subject from the 2025/2026 school year, rolled out in stages from grades 5, 7, and 10.
Interest in children's coding found its formal footing in 2025. Through Regulation of the Minister of Primary and Secondary Education Number 13 of 2025, the elective subject Coding and Artificial Intelligence officially entered the national curriculum for the 2025/2026 school year. The policy places digital skills inside the structure of schooling, while signaling to families that coding and AI are competencies the education system recognizes.
The subject's design emphasizes computational thinking, problem solving, and digital ethics. Its status as an elective, held open as a choice, leaves an important role for support outside school: families who want their child to go deeper into coding and AI beyond class hours seek kids coding classes and extra guidance. Policy opens the door, while interest determines how far a child steps through it.
Source: Ministerial Regulation No. 13 of 2025; Coding and Artificial Intelligence Subject Guide, Ministry of Primary and Secondary Education; EduPoint internal analysis (indexed).
Indonesia aims for nine million digital talents by 2030, while annual supply stays well below the need, a gap that explains why families prepare children early.
Behind interest in children's coding sits a long-range economic calculation. The ministry handling digital affairs projects a need for around nine million digital talents by 2030 to sustain the national digital economy. Averaged out, that need equals hundreds of thousands of new talents every year.
This gap between need and supply thickens families' willingness to invest in children's digital skills. Learning coding and artificial intelligence reads less like a pastime and more like preparation for fields of study and careers expected to stay open. Families who introduce programming and AI literacy from primary school place their child early on a long path.
Source: BAKTI, Ministry of Communication and Information Technology; reporting by ANTARA (state news agency); EduPoint internal analysis (indexed).
National internet penetration exceeds three-quarters of the population, and the youngest generation grows up most connected, preparing fertile ground for children's coding and AI interest.
Interest in children's coding and AI grows atop an increasingly mature digital infrastructure. The 2024 survey by the Indonesian Internet Service Providers Association (APJII) recorded national internet penetration at 79.5 percent, equal to 221.5 million people out of a population of 278.7 million. Indonesian children today grow up in connected homes, at ease with devices long before they meet their first line of code.
| Generation | Penetration rate | Share of users |
|---|---|---|
| Gen Z (born 1997-2012) | 87.02% | 34.40% |
| Millennials (1981-1996) | 83.00% | 30.62% |
| Gen X (1965-1980) | - | 18.98% |
Gen Z, which spans school pupils and early university students, leads with an 87 percent penetration rate and forms the largest user group. The cohort younger than them, children just entering school, inherits the same connectedness. This digital readiness underpins why kids coding classes and early AI literacy meet a quick reception.
Source: APJII, Indonesia Internet Penetration Survey 2024; EduPoint internal analysis (indexed).
Across the 2026 observation window, interest in children's coding and AI literacy both strengthened, with artificial intelligence growing fastest, from a base of 100 to 181.
The heart of this report is momentum: how fast interest in children's coding and AI strengthens. Measured as a relative index with the start of the observation window set as a base of 100, all three skill families move upward. Artificial intelligence literacy records the steepest momentum, followed by coding, then robotics.
| Skill family | Base | Window end | Direction |
|---|---|---|---|
| Artificial intelligence (AI) literacy | 100 | 181 | fastest rise |
| Coding / children's programming | 100 | 158 | rising |
| Robotics and electronics | 100 | 129 | rising |
The surge in AI literacy runs alongside the widening public conversation about artificial intelligence and the topic's entry into the curriculum. Coding remains a stable, growing foundation, since it is the most concrete gateway for a child to understand how smart machines work. Robotics, which demands hardware, grows more quietly yet consistently.
Because the observation window is short, this momentum reads as a direction and stays short of a firm forecast. The index values show the ranking of pace across families without revealing raw volume. The chapters that follow examine where this interest concentrates and at which grade it peaks.
Source: EduPoint internal analysis (indexed), children's digital skill interest February to June 2026.
Children's digital skill interest splits into three: programming holds the largest share, followed by artificial intelligence literacy, then robotics and electronics.
Within the basket of children's digital skills, programming holds the largest share. Coding, from visual programming to text languages, is the most common entry point because it is the easiest to practice with devices already at home. Artificial intelligence literacy follows as the second family, while robotics rounds out the set with a hardware dimension.
Relative share across families (percent), based on the internal interest mix February to June 2026. Absolute figures are withheld.
This composition mirrors a natural learning ladder. A child usually starts with coding as a foundation for computational thinking, then moves to AI literacy to make sense of the technology they use every day. Robotics draws the child who enjoys the meeting of program and moving object. The three reinforce one another within a single skills ecosystem.
Source: EduPoint internal analysis (indexed), children's digital skill interest February to June 2026.
Visual programming such as Scratch tops the children's coding skill mix, followed by Python, AI literacy, game development, and robotics.
Broken down into specific skills, the children's coding learning path follows a clear pattern. Block-based visual programming, with Scratch as its best-known face, holds the top of the interest ranking. Its building-block format lets primary-school children begin constructing logic without stumbling over syntax.
Relative index, top skill = 100. Absolute figures are withheld.
Python emerges as the bridge to text programming, chosen for its friendly syntax and wide use in the world of artificial intelligence. AI literacy and prompting skills climb fast, marking an awareness that understanding how smart machines work now matters as much as writing code. Game development becomes a motivation magnet, because a child sees their work turn immediately into something playable.
Interest appears as a relative index across skills, showing the order without revealing raw volume. The arrangement confirms that the children's coding path stands firmest when it starts with the most age-friendly step, then climbs with readiness.
Source: EduPoint internal analysis (indexed), children's digital skill interest February to June 2026.
Children's coding and AI interest concentrates in digitally ready cities, led by Greater Jakarta, then Surabaya, Bandung, and other major cities.
Interest in children's coding and AI spreads unevenly across the country. The Greater Jakarta metropolitan area holds the top distribution index, supported by a dense concentration of schools, wide internet access, and a mature technology ecosystem. Surabaya and Bandung follow as regional education and technology centers.
Relative index, top city = 100, aligned with digital readiness and school distribution. Absolute figures are withheld; cities with small samples are suppressed for representativeness.
This pattern aligns with the national digital readiness map. Urban internet penetration reaches 82.2 percent according to APJII, higher than rural areas at 74 percent. Cities with a concentration of middle-class households and technology infrastructure become the ground where children's coding interest takes root fastest, while growing regions call for a different bridge to access.
Cities with small samples are suppressed to preserve representativeness. The implication is that access to quality kids coding classes is thickest in large cities, and online learning carries an important role in shortening the distance to growing regions, a dynamic examined in the equity chapter.
Source: EduPoint internal analysis (indexed); APJII, Indonesia Internet Penetration Survey 2024.
Children's coding and AI interest peaks at upper primary and junior secondary grades, aligned with the entry points of the Coding and Artificial Intelligence policy at grades 5 and 7.
One of the sharpest findings in this report is the correlation between interest and grade level. Interest in children's coding and AI peaks at upper primary grades, followed closely by junior secondary. Interest at lower primary grades runs lower, tracking cognitive readiness, while senior secondary tends to shift toward preparation for academic selection.
Relative index, top level = 100. Absolute figures are withheld.
Strikingly, the interest peak falls exactly on the grades that serve as policy entry points. The elective subject Coding and Artificial Intelligence rolls out in stages from grade 5 and grade 7, the two levels with the highest interest index. This meeting of the curriculum agenda and family aspiration reinforces both: schools open the door, and families push the child to step further through extra kids coding classes.
The shift toward academic preparation at senior secondary explains the lower index at grades 10 to 12. Interest at this level stays alive, though it competes with the demands of university-entrance selection. For families, building a coding foundation from primary and junior secondary becomes a strategy so that digital skills take root before study schedules tighten.
Source: EduPoint internal analysis (indexed); Ministerial Regulation No. 13 of 2025.
Coding trains computational thinking that overlaps with numeracy, a domain where Indonesia's results still sit below the international average, so introducing coding carries value beyond technical skill.
The value of learning coding for children reaches beyond mastering a programming language. What it truly trains is computational thinking: breaking a large problem into small steps, recognizing patterns, and building a solution in stages. This ability overlaps closely with numeracy, the skill of applying mathematical reasoning in real life.
| Domain | Indonesia | OECD average |
|---|---|---|
| Mathematics | 366 | 472 |
| Reading | 359 | 476 |
| Science | 383 | 485 |
The Programme for International Student Assessment (PISA) 2022 places Indonesia's mathematics score at 366, well below the OECD average of 472. The figure shows a wide room for improvement in quantitative reasoning. Coding offers an enjoyable gateway into logical and mathematical reasoning, because a child sees the direct effect of every step they assemble.
Against this backdrop, interest in children's coding carries a double benefit. It prepares the digital skills the future economy seeks, while training the reasoning that supports core school subjects. Families who introduce coding early are also, in effect, tending a broader foundation for thinking.
Source: OECD, PISA 2022 Results (Indonesia); EduPoint internal analysis (indexed).
Uneven digital readiness between urban and rural areas shapes who can most easily access children's coding and AI, making infrastructure equity the key.
The concentration of children's coding and AI interest in large cities raises a question of equity. Access to kids coding classes and AI literacy rests on two conditions: a device and a connection. Neither is yet evenly spread across the country.
The urban-rural gap bears directly on the interest map. A child in an area with a stable connection and adequate devices finds it easier to try visual programming, reach AI literacy material, and join online classes. A child in a growing region faces higher barriers, even when their interest and potential match.
Online learning offers a way to shorten the distance. Online kids coding classes let a child in a small town learn from a quality tutor wherever that tutor is, as long as a connection is available. For that reason, expanding rural digital infrastructure moves in step with widening access to future skills.
Source: APJII, Indonesia Internet Penetration Survey 2024; EduPoint internal analysis (indexed).
Children's coding and AI tutors are led by Informatics and Computer Science backgrounds, followed by Information Systems and related engineering, with computer education adding pedagogy.
The quality of a kids coding class rests on who teaches it. The educational background of coding and AI tutors is led by the Informatics and Computer Science families, the fields closest to programming and artificial intelligence material. Information Systems and Electrical Engineering round it out, especially for topics that touch devices and robotics.
Relative index, top major = 100. Absolute figures are withheld.
The presence of a Computer Science Education background matters for teaching quality, since it brings formal pedagogy alongside command of the material. A Mathematics background also colors the mix, in step with the close overlap between coding and quantitative reasoning. This composition shows that children's coding tutors stand at the crossing of technical command and the ability to explain.
Source: EduPoint internal analysis (indexed); PDDikti, Higher Education Statistics 2025.
Children's coding sessions lean more online than the private-tutoring average, because the material is screen-based, though in-person still leads for early guidance.
The way children learn coding and AI has a distinctive shape. Because the material is screen- and project-based, online learning holds a larger share than the private-tutoring average. Even so, in-person still leads, especially for a child who is just starting and needs direct guidance.
Relative share across modes (percent), based on the children's coding and AI interest mix February to June 2026. Absolute figures are withheld.
This online share of around 38 percent runs higher than the private-tutoring average, which rests on in-person delivery for most of its sessions. The software-based nature of coding makes physical distance a smaller barrier: a child and tutor can share a screen, run code together, and review results directly from different locations.
Source: EduPoint internal analysis (indexed), children's coding and AI interest February to June 2026.
The index findings guide steps for families, service providers, and policymakers in children's digital education.
AI literacy will keep leading the momentum, coding stays the foundation, and curriculum rollout reinforces interest, while a longer data range is awaited.
Three directions appear to strengthen. First, artificial intelligence literacy will continue its fastest momentum as smart technology grows deeper in a child's daily life. Second, coding stays a stable foundation, since it is the most concrete gateway to computational thinking. Third, the staged rollout of the Coding and Artificial Intelligence subject will reinforce interest at upper primary and junior secondary grades.
This report is a single-period snapshot, so its projections are indicative. A later edition will widen the time range so that seasonality and year-over-year change read more fully, moving toward a sustained index of children's digital skill interest.
This report combines two data lanes. The internal lane draws on interest on the EduPoint platform and appears in indexed form (index, rank, or share), without absolute figures. The external lane draws on official sources and is cited directly. This separation protects confidentiality while preserving credibility.
Internal interest data covers February to June 2026. Because this range is short, the momentum index reads as a direction within the observation window and stays short of an annual projection. Policy and seasonality claims rest on official documents.
Children's coding interest is counted from interest in programming skills, artificial intelligence literacy, and robotics for school-age learners. Labels referring to the same skill are merged into one family. Grade levels follow the national school classification.
All internal magnitudes are converted into indices, ranks, or relative shares. No absolute counts, rupiah rates, or business metrics appear. The momentum index uses the observation-window start as a base of 100. The aim is to present the structure and direction of interest without revealing operational data.
The city distribution index is aligned with official digital readiness (APJII internet penetration) and school distribution so that it reflects the true structure of interest and stays free of sample distortion.
Data cells with small samples are suppressed to preserve representativeness and privacy. The reading focuses on cities, grades, and categories with adequate data support.
As a single-period snapshot, this report does not yet capture full seasonality or year-over-year change. The momentum index is indicative. A later edition will enrich the time range so that long-term trends in children's coding and AI interest can be read.
Appendices A
A summary of all indices used in this report. Every value is relative (indexed) and does not represent an absolute count.
| Family | Base | Window end |
|---|---|---|
| Artificial intelligence (AI) literacy | 100 | 181 |
| Coding / programming | 100 | 158 |
| Robotics and electronics | 100 | 129 |
| City | Index |
|---|---|
| Greater Jakarta | 100 |
| Surabaya | 61 |
| Bandung | 58 |
| Medan | 44 |
| Yogyakarta | 41 |
| Makassar | 33 |
| Grade level | Index |
|---|---|
| Upper primary (grades 5-6) | 100 |
| Junior secondary (grades 7-9) | 92 |
| Senior secondary (grades 10-12) | 64 |
| Lower primary (grades 1-4) | 43 |
| Mode | Share |
|---|---|
| In person | ≈54% |
| Online | ≈38% |
| Hybrid and projects | ≈8% |
Appendices B
Adhel Pradita Sugito, & Edupoint Researcher Team. (2026). Children's Coding and AI Interest Index 2026: Momentum, Cities, Grades. EduPoint Education Research. EduPoint Indonesia. https://edupoint.id/en/research/report/indeks-minat-coding-ai-anak-2026
@techreport{edupoint-indeks-minat-coding-ai-anak-2026-2026,
title = {Children's Coding and AI Interest Index 2026: Momentum, Cities, Grades},
author = {Adhel Pradita Sugito and Edupoint Researcher Team},
institution = {EduPoint Indonesia},
type = {EduPoint Education Research},
year = {2026},
url = {https://edupoint.id/en/research/report/indeks-minat-coding-ai-anak-2026},
}EduPoint connects families with quality coding and AI tutors across Indonesia, from visual programming for beginners to artificial intelligence literacy.
Find a Kids Coding Tutor