AI and Education: The End of the Age of Rote Instruction?

Thirty children in one classroom receive the same lesson at the same pace, then their intelligence is measured by what they recall on exam day, while each carries a different map of knowledge. Many historians of education trace this model to the spread of formal schooling with industrialisation and its need for disciplined workers who answer to the bell, not to educational psychology alone. We inherited a design built for quantitative efficiency, not individual growth.

The two-sigma problem

In 1984, Benjamin Bloom presented results from small studies he supervised, in which learners given one-to-one tutoring combined with mastery learning outperformed the conventional class by about two standard deviations (2 Sigma). The result has not been replicated at that size; later meta-analyses put estimates at around 0.3 to 0.4. He asked his painful question: how do we give every child a private tutor without bankrupting society? It hung unanswered because the answer was economically impossible. Today a possible answer glimmers: an algorithm that never tires and costs far less than a tutor for every child, if it is used well.

The revolution in diagnosis

What separates intelligent systems from the textbook is not a dazzling display, but that they read the learner as they learn: where they hesitate, how long they take, which mistakes they repeat. They build a living model of the learner’s mind. Personalised learning draws its power from precise targeting: it offers the exercise at the edge of the learner’s ability, neither the easiest, which bores, nor the hardest, which discourages. In some studies, intelligent tutoring systems approach the effect of a human tutor (VanLehn, 2011). The group classroom has never managed this in its history; one teacher cannot hold thirty maps in mind at once.

Not the end of memory, but the collapse of rote instruction’s value

Popular talk mourns the age of memorisation, which is true but incomplete. What is meant here is memorising transient information; memorising the Book of God and the scholarly texts is worship and a foundation for building, and falls outside this discussion. The value of recalling a fact in the knowledge market has collapsed now that it is a question away. Education that rewards memory at the expense of understanding prepares our children to compete with the machine in the one field where it will inevitably win. It is wiser to steer the mind towards what algorithms do not yet do well: the right question, connecting distant ideas, and ethical judgement. The World Economic Forum’s Future of Jobs Report (2025 edition) places analytical thinking, resilience and creative thinking among the most in-demand skills. Even so, firm memory is the soil of understanding; the mistake is to make memorising an end rather than a foundation.

What may be lost along the way

• Cheating and intellectual dependence: when thinking itself is handed to the machine, the mental effort that builds understanding weakens, and homework turns into cheating.
• Measurement that misses what matters: if we hand the machine the definition of success, our goal becomes what it can measure, not what deserves measuring. Wisdom, taste and moral courage resist reduction to a number.
• Filter bubbles: excessive personalisation can lock the learner into a mould made by data, denying them the chance to surprise themselves.

The teacher’s role rises

The ideal formula is not to replace the teacher with the machine but to redefine the teacher’s role: the algorithm takes over repetitive practice and assessment, and the teacher is freed for what cannot be assigned to a machine, to be a mentor walking beside the learner and a witness to another human being’s journey. Learning is a human relationship before it is a transfer of information, and gentleness towards the learner who errs and stumbles is part of teaching that no report or algorithm can stand in for. This is an elevation of the teacher’s standing, not a surrender of it, and the family and school have an indispensable share in it.

Pillars of implementation

1. Technology is a tool, not an end, serving a clear educational philosophy.
2. Learners’ data is a trust: a map of children’s minds that, in commercial hands, would become an instrument of control (see the article “Privacy in the Age of AI”). UNESCO’s Recommendation (2021) makes human dignity and rights the foundation of any use of AI, education included.
3. Fairness of access: if excellent education becomes the preserve of the well-off, the gap we were promised to close will widen.
4. Teacher training is the cornerstone.
5. Education in critical thinking: a learner who converses with the system and questions it when needed.

The Saudi opportunity

Saudi Vision 2030 puts people at the heart of its development project and makes building creative minds a strategic goal, and AI-supported personalised learning is a lever for the knowledge economy. Success depends on localising these systems in our language and values, not importing them in translation.

The end of the age of rote instruction is not the end of knowledge, but a redefinition of what deserves to be known and how. If we tame the machine well, we may see a school that does not ask the child to be shaped to its measure, but shapes itself to the child’s. The question of who holds the helm remains, and in it the features of our coming generations’ education will be drawn. We ask God for guidance.