Why The Struggle Of Reading Brain Must Not Be Automated
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As countries move toward large-scale implementation of AI in schools, a global narrative has taken hold: personalised learning, adaptive tutoring, and early AI literacy are being framed as inevitable upgrades to education. El Salvador has partnered with Elon Musk’s xAI to introduce AI tutoring through the Grok chatbot to millions of children in public schools; Microsoft has announced plans to roll out AI tools and training to 200,000 students and educators; and Kazakhstan has entered an agreement with OpenAI to provide ChatGPT Edu while embedding AI tools and standards into its national education framework. Similar ambitions are now visible here in India as well.
What this rush rarely pauses to ask is a more uncomfortable question: what kind of learning are we optimising for and what kind are we quietly discarding? Reading, often treated as a basic skill that AI can ‘support’ or ‘speed up,’ offers a crucial test case. It is one of the most studied, effort-dependent, and developmentally fragile forms of learning we know. It is very difficult that reveals what automation cannot replace.
Unlike speaking, reading is not evolutionarily built into the human brain. There is no dedicated ‘reading centre’. When children learn to read, the brain must painstakingly re-purpose and synchronise systems evolved for other tasks: visual recognition, sound processing, motor control of eye movements, language networks for meaning and syntax, and attention and working memory to hold everything together. This coordination is slow, effortful, and developmental, and the effort required is not a flaw; it is the point.
Children learn through a gradual, often frustrating, process: they discover that marks represent sounds, sounds form words, and words convey meaning across contexts. These capacities do not stop at literacy. They are the foundations for mathematics, scientific notation, music, and later abstract reasoning. When learning is made frictionless too early, these foundations weaken.
When a machine reads for a child or supplies instant meaning, it removes the attentional labour through which executive function and metacognition are built. Reading also trains time-based cognition: tracking sequence, cause and effect, anticipation, and revision. These are capacities central not just to reasoning, but to judgment, planning, and ethical reflection. AI collapses time by delivering conclusions instantly, denying children the experience of living inside the slow unfolding of meaning.
As children read, they learn to monitor themselves: This doesn’t make sense. I need to reread. Metacognition develops not through instant correction, but through difficulty that is recognised and worked through. They learn to stay with frustration, suspense, and curiosity, discovering that effort leads to understanding. Over time, reading becomes silent, and an inner voice emerges—a voice that narrates, questions, evaluates, and imagines alternatives. AI supplies external language. Reading builds internal language.
Consider the difference between listening to a story and struggling through one independently. When children sound out words haltingly, they are doing far more than decoding text. They are learning persistence, attention, and self-regulation. Cognitive research has long shown that such ‘productive difficulty’ strengthens memory, comprehension, and long-term learning. A shortcut that removes the struggle also removes the growth.
When children realise that they can unlock meaning on their own, the text stops being something that happens to them and becomes something they actively enter. This autonomy, which is built through effort, error, and persistence, is foundational to independent thought. Books support this process in deeply embodied ways. Children can hold them, turn pages, pause, wander, and return. Reading unfolds at the pace of the body as much as the mind.
AI tutoring systems, by contrast, often require children to sit upright, face a screen, and sustain attention in ways that are externally regulated and developmentally taxing. Extended, screen-based instruction fragments attention, increases cognitive fatigue, and reduces opportunities for self-directed engagement, especially for younger learners.
Reading with adults—parents, teachers, grandparents – adds another dimension that machines cannot replicate. Shared reading involves eye contact, laughter, explanation, correction, and encouragement. It teaches children that language connects people, that understanding grows through interaction rather than instant delivery. An AI voice, however fluent, cannot offer relational feedback or emotional attunement. Nor can it fully navigate the multilingual and multicultural realities of Indian classrooms—work that still depends on human judgment and care.
If reading is treated merely as efficient information access, machines will always do it better. But if reading is understood as a formative practice, one that shapes attention, judgment, empathy, and responsibility, then its value lies precisely in what cannot be optimised. The ability to read slowly, sceptically, and contextually does not appear spontaneously in adulthood. It is cultivated early, through the demanding human work of learning to read.
The question, then, is not whether AI can assist literacy—it can. The real question is whether we are willing to protect the developmental space that reading creates. Learning to read teaches children how to be with language before language acts upon them. That lesson is too important to outsource.
The views and opinions expressed in this article are those of the author and do not necessarily reflect the views or the positions of the organisation they represent.
The author is a faculty at Azim Premji University
