Let me tell you about a girl named Ananya. In Class 3, she was the child her teacher quietly worried about. Not because she was struggling—her grades were fine. But she never raised her hand. She sat at the back and avoided eye contact when questions were asked aloud. Her parents had noticed it too—a child who seemed to shrink a little more each year, convinced she wasn’t the kind of person who had good ideas.

By Class 5, Ananya was presenting a working prototype of a mobile app she had built—to her entire school, on stage, without notes. There was no special coaching or dramatic turning point.

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Neuroscience, it turns out, has a very precise explanation for what happened to her. This story was narrated in the literature of an Indian EdTech provider to encourage schools to introduce primary students to gamified AI and coding. They argue that learning to code “rewires” the brain, activates the prefrontal cortex, and triggers dopamine release. As their literature noted: “Struggle stops feeling like failure. It starts feeling like the process.”

It is, however, not picture perfect. In 2016, industry leader Lumosity paid a $2 million settlement because they lacked evidence that their games improved brain function. Today, when Indian EdTech providers are asked for longitudinal clinical data to back their “rewiring” promises, many remain silent. For India’s 260 million children, this suggests that these bold marketing claims may not yet be supported by genuine science.

The success loop: Engineering confidence

At the heart of this transformation is a psychological mechanism known as a “confidence loop.” Promotional materials of the EdTechs specialising in gamified environments suggest that every solved challenge triggers an immediate reward—a badge or experience points. This feedback is designed to activate the brain’s reward circuits, creating a sense of “mastery” that encourages the student to tackle the next task.

While industry rewards are digital, some parents believe real confidence comes from physical results. R. Nagarajan, a parent in Chennai, noticed his child only understood the value of coding when it affects the physical world. “What helped my kid was having something hands-on where the code actually changed something right in front of them,” he said. “Once they got a couple of small wins, they were much more open to moving past the block stuff because it finally felt useful instead of abstract.”

Neuroplasticity vs. dopamine spike

This raises a critical question: can digital success truly foster long-term neuroplasticity?

Prof. Koumudi Patil, Associate Professor at IIT Kanpur researching education design, urges caution on this. “Any claim in this area demands considerable and reliable longitudinal data for analysis, which is unavailable. However, I can explain precisely why generalisation remains premature and should not be attempted,” she says.

She notes, “At the outset, genuine neuroplasticity resulting from skill acquisition and a temporary dopamine spike can look nearly identical. Both produce increased engagement, visible enthusiasm, and an upward learning trajectory. The divergence only becomes apparent over time.”

Prof. Patil explains that a dopamine spike is short-lived. As the technology becomes familiar, the novelty fades. “Genuine neuroplasticity, by contrast, sustains, much like long-term memory, it becomes part of the learner’s cognitive architecture rather than a response to a stimulus. Real skill change tends to transfer across domains. Dopamine-driven responses tend to remain confined to the specific context in which they arose.”

The transfer problem: Why games aren’t enough

“Dopamine-driven, gamified engagement does not automatically translate into real-world cognitive or decision-making gains,” Prof. Patil says. “Transfer is highly conditional. It depends on the frequency of exposure, the degree to which real-world contexts are meaningfully mapped onto the gamified environment, and whether the reward structures of the game find some parallel in the learner’s actual world.”

Prof. Patil shared an example from her own lab to show how difficult it is to claim changes in a child’s brain are permanent. She created a Math game called ‘Challan’, that was based on the real-world Math children already use in their daily lives. While the students did better in school for a short time, she is honest about the limits of the results.

“I can only confidently claim that the effect persisted for one month. We did not conduct a longitudinal study, and so what happened beyond that window remains unknown,” she says.

She warns that a one-month boost is not proof of a permanent change in the brain. More importantly, she notes that what works for one group of children might not work for another. Unless a company can demonstrate longitudinal evidence, drawn from a sample of acceptable size and rigour, specific to their intervention, their claims cannot be evaluated.

The Policy context: The 2026 mandate

This “neuro-marketing” is fueled by a massive shift in national policy. With a 2026 mandate requiring AI education as early as Class 3, schools are under pressure to modernize. Lacking internal resources, many turn to private EdTech platforms out of necessity.

These platforms do more than teach logic. One provider, working with 200 schools across South India, including Karnataka and Tamil Nadu, uses the RIASEC framework to sort students into vocational types before they reach high school. Consequently, schools are creating separate learning tracks based on these algorithmic profiles. For students like Ananya, the stakes are high: are these tools opening doors, or using “black box” algorithms to funnel children into career paths before their interests have truly formed?

The RIASEC black box

While Ananya was building her app, a hidden algorithm was deciding her future. This system, called RIASEC, labels children as ‘types’ before they even reach high school. Dr. John Holland developed the Holland Occupational Themes in the 1950s. The framework is grounded in the premise that vocational choice is fundamentally an expression of an individual’s personality. His theory categorises individuals and work environments across six themes, viz., Realistic, Investigative, Artistic, Social, Enterprising, and Conventional. Hence, the model is also commonly known as RIASEC.

RIASEC rests on the assumption that individuals have been exposed to a sufficiently wide range of occupations to form meaningful interests. For EdTech, the algorithm tracks how a student interacts with digital challenges to build a unique behavioral profile. Based on this data, it automatically assigns children to specialised paths, such as “Space Tech” for those with realistic traits or “Finance” for those identified as enterprising. Schools then use these digital labels to strategically funnel students into different workshops and learning modules. This process essentially creates “separate tracks” for ten-year-olds, potentially narrowing their educational world before their true interests have even formed.

The risk of early labeling

The teacher, R. Deepak, says that children start with different skill levels. “The biggest challenge to teaching young students is that they arrive at programming with differing levels of computational thinking skills,” says R. Deepak, an educator in Andhra Pradesh. He explains that using block-based tools helps ease the “cognitive load,” allowing students to become proficient in core concepts before tackling complex syntax.

However, using these interactions to predict a career is where the science wears out.

“Younger children consistently tend to produce low differentiation scores, making it difficult to identify a dominant personality type or occupational direction. It is only in late adolescence that interest profiles typically sharpen into meaningful patterns,” says Prof. Patil. “Applying RIASEC before this developmental threshold is risky. It can draw conclusions from data that the instrument was not designed to produce.”

Structural inequality in India

Applying western tools to India often ignores structural realities. “In India, however, exposure to occupational diversity varies enormously, shaped by geography, socio-economic status, caste, gender, and structural inequality,” Prof. Patil explains. “Where exposure is absent, interest cannot emerge. It would be incorrect, for instance, to conclude that rural children have no aptitude for cybersecurity. In the absence of any encounter with the field, the assessment will simply record no interest, which is not the same as no potential, and hence no career.”

While EdTech companies use algorithms to find a child’s direction, Prof. Patil says, “An exposure to emerging fields like AI, coding, biotechnology, space science, and design thinking should be seen as ways to broaden children’s thinking, not as early career specialisation. The goal is not to produce coders or astronauts at age seven. It is to expand the range of problems a child can think about and the range of tools they feel confident using.”

“A future-ready child is not one who has specialised in a particular field but one who can move between fields and combine them when needed. A child who says ‘I don’t know what I want’ is not lacking direction. They are still discovering the world, which is exactly what childhood is for. Indecision at this stage is not a problem, to be fixed by early assessment is one. Interests grow through exposure, doing, failing, and iterating again. Future readiness is not about which problems a child has learned to solve. It is about whether a child can solve problems they have never been taught to solve,” she adds.