DEV Community: Mazhar Iqbal

Things You Need to Know Before Trying to Teach Coding for Kids

Mazhar Iqbal — Mon, 25 May 2026 10:31:15 +0000

Most developers see their children as future lead engineers. We want to share the logic we love. A Saturday morning might be a good time to buy a robot kit or open a terminal. However, teaching coding for kids is very different from onboarding a junior developer. A successful first line of syntax depends on mental preparation.
Many parents who code professionally wonder where to start, and soon realize that structured support, like an elementary math tutor, can help build the logical foundations kids need before they write their first line of code.
Why Age and Readiness Matter More Than Enthusiasm When You Teach Kids to Code

It's important to teach kids to code at the right time. Excitement is not the only factor. Kids often lose enthusiasm when they run into a difficult bug. Unlike younger kids, older kids are able to handle failures with patience. In contrast, they do not give up as easily. Before typing, these children think through the problem. As they grow, they usually develop this logical readiness. Children can pick up words quickly when they are young. Older brains, however, have a better understanding of how programs work. Starting too early can make the struggle too difficult. It might even make them dislike technology.
What research says about kids and abstract thinking

According to Jean Piaget children around ages 7–11 enter the “concrete operational stage,”
where they begin to think logically about concrete situations and understand concepts such as cause and effect. However, abstract ideas and hypothetical reasoning are still difficult at this stage.
The "formal operational stage," which normally starts at age 11 or 12, is when abstract and systematic thinking usually emerges. This stage is linked by researchers to the comprehension of variables, functions, and increasingly complex logical systems, such as programming principles.
Signs your child may be ready to start
Watch if the kid is interested in board games or complex puzzles. When they can follow multi-step instructions to build something, like Lego, they have the focus. In logic-based subjects, academic progress is also a positive sign. A child who can solve word problems well will most likely be able to solve basic loops as well.
The Math Connection: Why Numeracy Comes Before Syntax
Coding is a form of applied mathematics. There is a similar neural pathway. Coding for kids will be difficult for a child who struggles with numbers. Before opening an IDE, parents should prioritize numeracy.
How logical and sequential thinking maps to coding concepts
Math helps kids recognize patterns and follow sequences. A simple addition problem is actually a basic algorithm. Sorting shapes or toys prepares the brain to understand complex data structures. When a child solves for a variable in a math problem, they are learning how logic gates work. Mastering these math basics makes writing code feel like a natural next step.
What to do if your child struggles with math before you start coding
Don’t make children suffer; it's better to address the gaps to professionals. For example, Brighterly is a 1:1 learning platform that offers personalized math lessons, which is the foundation for coding. Start with small steps to better understand how to teach children programming.
Choosing the Right First Language
Don't start with the stack you like best. Beginners will be overwhelmed by C++ or Java. The biggest motivation killer for young learners is syntax errors. It is necessary to use tools that provide instant, visual feedback.
Block-based tools
Visual blocks solve the "missing semicolon" problem. It is more important for kids to focus on the flow of logic than spelling. The tools allow users to drag and drop loops, events, and variables. Until students can build a multi-level game independently, they should stay here.
The case for games and puzzles
Take part in "unplugged" activities to get started. Write a "code" for moving a toy across the room using grid paper. Put sequential thinking to work by using puzzles. Play is a better way for kids learn programming concepts than screens alone.
Common Mistakes Developer Parents Make
We often forget how we learned. We also forget that our kids have different interests. Forcing a career path usually backfires.
The "I'll just teach them how I learned" trap
Despite being a professional, you may not be a teacher. Parents learn too fast. They skip the "boring" basics because they seem obvious to them. A professional educator knows how to break concepts into tiny, digestible bites.
Keeping Motivation Alive
To keep motivation alive, try to:
● Honor any small script that succeeds
● Focus on entertaining game projects
● Take breaks to avoid burnout
● Change to real-world logic puzzles
● Set daily coding goals
Structuring Learning Sessions That Actually Stick
Continuity is more important than intensity. It takes time for the brain to process new logical frameworks.
Short-burst learning vs. marathon sessions — what works for kids
Since young children are still developing attention spans and memory skills, short, focused sessions (5-10 minutes) are more effective for them. During long lessons, kids can lose focus and become tired, which makes learning less effective.
John Hattie found that children learn better if they practice over a longer period of time in his 2023 research review. Study sessions that are shorter and repetitive tend to improve academic performance more than lessons that are longer and more intensive.
When introducing new challenging material, it’s better to have a 20-30-minute session. So you have time to explain, and the child has time to ask the questions.
Free resources vs. structured programs
Free resources Structured programs
✅Zero financial risk for parents ✅Kids stay focused when they have clear milestones
✅Wide variety of coding games ✅Professional tutors resolve challenging issues
✅Immediate access to basic tools ✅Proven learning science is used in curricula
✅Encourages independent trial and error ✅Parents receive regular updates on their children's progress
🚩Logical progression is frequently absent from content 🚩Logical progression is frequently absent from content
🚩No professional criticism for mistakes 🚩Additional expenses
🚩High chance of contracting insects 🚩May feel like extra school
You can always find quality math classes for kids that follow a structured program.
When to Step Back and Let Someone Else Teach
● You both feel exhausted after learning sessions.
● You find it difficult to communicate ideas to your child in a way that they can grasp.
● Despite your explanations, they continue to make the same errors.
● Instead of seeing your comments as guidance, your youngster interprets them as criticism.
Hire a tutor if your child gets defensive when you correct their code. Having an external mentor keeps the hobby fresh and interesting.
What to look for in a STEM program
Groups can distract kids who have trouble focusing, so look for programs that offer 1:1 interaction with elementary math tutor. Make sure the curriculum matches their current grade level.
Conclusion
It takes a lot of time and effort to teach a child how to code, but taking the right steps at the right time requires patience. First, focus on the logical foundations. Use age-appropriate tools. The most important thing is to keep the experience light. The goal is to foster curiosity, not to produce a senior developer by middle school.

The Classroom Gap: Why Applied AI Has Yet to Transform How the World Learns

Mazhar Iqbal — Fri, 22 May 2026 20:07:35 +0000

A global hackathon is attempting what the broader industry has so far failed to do - move AI from the research lab into the classroom. The effort reflects a growing recognition that education may be one of the most consequential, and most underserved, frontiers for applied machine learning.

There is a striking discontinuity at the heart of the current AI moment. Systems capable of synthesising complex research, generating production-grade code, and sustaining nuanced multi-turn dialogue across dozens of languages are now widely accessible. Yet the inside of a typical classroom looks remarkably unchanged. Students in underfunded systems still work from outdated materials. Teachers stretched thin across large cohorts still spend a disproportionate share of their time on administrative work rather than instruction. And AI tutoring architectures sophisticated enough to adapt in real time to individual learners largely remain confined to research environments or niche commercial products that never reached the schools that need them most.
The gap is not primarily technological. The models exist. The infrastructure exists. What has been missing, according to the organisers of EdTech 3.0, is a critical mass of builders who understand both sides of the problem, the machine learning architecture and the lived pedagogical reality, working together under conditions that demand deployable results.
EdTech 3.0 is a week-long global hackathon running from June 18 to 25, 2026, produced under Open Source Connect, an international open-source community initiative. Its stated ambition is to close the distance between what AI can do and what classrooms actually use — not through research proposals or polished demos, but through software that could plausibly operate in a real school by September.
The Scale of the Problem
The education deficit that AI might address is not a marginal policy concern. An estimated 300 million children worldwide receive an education so inadequate it provides little meaningful preparation for adult life. Teachers in under-resourced systems spend upwards of 40% of their working hours on administrative tasks, grading, progress documentation, and lesson reporting - time that cannot be spent on the individual attention that research consistently identifies as the most effective driver of learning outcomes.
Meanwhile, the technical components for genuine AI-assisted education have matured considerably. Large language models can now maintain coherent pedagogical dialogue, identify knowledge gaps in student responses, and adapt explanations in response to demonstrated misunderstanding. Multimodal systems can process speech and text across languages. Voice synthesis and transcription tools have reached a quality threshold sufficient for classroom deployment. The bottleneck is not the underlying capability, it is the absence of domain-specific applications built to the standards that real educational contexts require: reliability, accessibility, usability by non-technical teachers and students, and sensitivity to the specific constraints of under-resourced environments.
Structure Over Spectacle
Most technology competitions produce what the hackathon format naturally incentivises: polished presentations optimised for a brief moment of evaluation. EdTech 3.0's organisers have tried to design explicitly against this tendency. The seven-day format is longer than most comparable events - long enough, in theory, for teams to move beyond proof-of-concept into architectures with genuine depth and documented behaviour.
The event is structured around four challenge tracks, each mapped to a documented problem in education with specific technical framing. The first concerns intelligent tutoring, building AI agents that don't simply return correct answers but genuinely model a learner's current state of understanding and adapt accordingly. The second addresses assessment and feedback automation: the administrative layer that consumes teacher time, where the goal is not merely to mark answers right or wrong but to generate contextual feedback that supports improvement. The third track focuses on accessibility and inclusion, with an emphasis on reaching learners who face structural barriers — language, disability, geography, and unreliable connectivity - that most edtech products tacitly assume away. The fourth track, the most demanding of the four, is reserved for teams with access to live educational partners: actual schools, tutoring centres, or learning programmes willing to participate in real-time testing.
The last of these is significant. It creates a pathway - rare in the hackathon format - from competitive prototype to documented real-world evidence within a defined timeframe. Projects in that track are evaluated with particular weight on demonstrated outcomes: teacher responses, observable changes in student engagement or performance, and evidence of usability outside a controlled environment.
Evaluation as Signal
The credibility of any competitive event depends substantially on the rigour of its evaluation. EdTech 3.0's scoring framework is worth examining on its own terms, because it reflects a set of priorities that differ meaningfully from the generic rubrics common to the format.
Educational impact accounts for 30% of the overall score - not as an aspirational criterion but as a requirement for specificity: which learners benefit, under what conditions, and at what projected scale.
Another 30% examines agent intelligence and autonomy: whether the system genuinely reasons, adapts, and handles edge cases, or whether it provides consistent responses regardless of context. The remaining 40% is split between scalability and user experience, with the UX criterion explicitly defined as usability by its intended audience without technical guidance. This is not a low bar for products targeting teachers who may have limited time and no engineering background or students in settings where digital literacy cannot be assumed.
The judging panel draws on genuine breadth of expertise. Evaluators come from major technology companies, including those with significant research and product investment in AI, alongside globally ranked universities, AI safety research organisations, and international institutions with operational experience in deploying educational programmes in resource-constrained environments. That last category is notable. The presence of evaluators with field experience in multilingual and underserved contexts signals an intention to assess submissions not only for their technical sophistication but also for their relevance to the students who stand to benefit most from better educational tools.
The panel also includes practitioners from across the AI product development lifecycle, from large-scale systems engineering to consumer product management to venture-stage product development, which means submissions will be assessed for commercial viability and real-world usability as well as technical ambition. For participants with serious intentions, this is closer to the scrutiny of a product review than a typical competition assessment.
Why Education, Why Now
The broader context for EdTech 3.0 is worth stepping back to consider. AI-in-education is not a new category - adaptive learning systems, automated essay scoring, and intelligent tutoring prototypes have existed in various forms for decades. What has changed is the underlying capability of general-purpose language models, which have collapsed the cost and complexity of building systems that can engage meaningfully with open-ended educational content.
This shift creates both an opportunity and a risk. The opportunity is that the barriers to building genuinely useful AI tutoring and assessment tools have fallen significantly. A team of engineers with access to a capable language model and a well-designed application layer can build something that would have required a dedicated research programme a decade ago. The risk is that the resulting products, if built without deep understanding of pedagogical context, will be superficially impressive and practically useless — or worse, systematically biased in ways that disadvantage the students who most need support.
EdTech 3.0's track architecture reflects an awareness of both dynamics. The emphasis on accessibility, inclusion, and real-world evidence is not incidental — it is an attempt to orient competitive incentives toward the harder, more important problems, rather than toward the solutions that are easiest to demonstrate.
The open-source ethos of the parent initiative, Open Source Connect, adds another layer of significance. Projects built at the event are intended to be visible, shared, and iterated upon — not locked inside a startup's proprietary stack or a research institution's internal repository. The argument, implicit in the event's structure, is that education is a domain where open contribution and community iteration have a role to play alongside commercial development.
The Practical Calculus for Builders
For ML engineers, product designers, and founders considering whether to commit a week to the event, the calculus involves several considerations beyond the prize structure.
The judging panel includes practitioners from companies that hire at the frontier of AI product development. A submission that performs well against the event's rubric, demonstrating genuine agent reasoning, real-world scalability, and usable design - functions as a form of professional evidence that is difficult to manufacture in an interview or portfolio context. For researchers, the Track 4 pathway offers something rarer still: a structured mechanism for connecting academic interest in educational AI to documented real-world evidence within a defined period.
For early-stage founders, the event offers structured expert feedback before a longer development commitment. Several of the evaluators specialise specifically in assessing whether a product has validated a genuine user need, the kind of scrutiny that can save months of building in the wrong direction.
Perhaps most importantly, the event reflects a view, increasingly shared across the AI industry, that education is not a peripheral application domain but one of the highest-leverage arenas for applied AI development. The systems that successfully bridge current AI capability and genuine classroom utility will define a significant industry. The work being done to build them, and the builders doing it, are worth watching.
EdTech 3.0 runs online from June 18–25, 2026. Registration is free and open globally. Details and team formation resources are available at ai-in-edtech.com.