AI as the New Banking Model of Education: Deposits, Withdrawals, and the Death of Curiosity

Written by Dr. Fariha Gul

Researcher, Educationist, Writer

Paulo Freire’s concept of the banking model of education has long been a critique of traditional teaching. In this model, students are seen as passive accounts into which teachers deposit knowledge, later expecting exact withdrawals during exams. The model reduces learning to mechanical storage and retrieval, stripping away critical thinking, creativity, and agency.

With the rise of artificial intelligence in education, the banking metaphor is resurfacing — not as a cautionary tale, but as a new operational reality. AI systems are increasingly being used to automate the “deposit” of pre-packaged knowledge and the “withdrawal” of standardised answers. While efficient, this transformation risks re-entrenching the same passivity Freire sought to dismantle.

AI as the New Teller

Where human teachers once served as depositors of knowledge, AI now functions as a 24/7 automated teller. Learning platforms powered by AI can instantly feed students summaries, explanations, and solutions tailored to curriculum goals. Adaptive learning algorithms even make “interest-earning deposits,” predicting what the learner might need next.

However, like a banking system that privileges liquidity over long-term investment, AI’s efficiency prioritises quick access to answers rather than deep cognitive engagement. The learner’s intellectual independence may be eroded if the “withdrawal” of knowledge becomes the default habit.

Data as Currency

In this AI-powered model, student data becomes the most valuable currency. Every click, quiz attempt, and discussion post is logged, analysed, and converted into refined educational products. Instead of knowledge being the deposit, personal learning profiles and behavioural analytics are the true assets — owned and managed not by the learner, but by the platform or institution.

This raises ethical and policy concerns:

Who owns the educational currency?

What interest rates are applied to our intellectual labour?

What happens when the bank fails — or changes its lending policies?

From Critical to Transactional Learning

If AI continues to be framed merely as a delivery mechanism for curriculum, we risk amplifying the transactional nature of learning. In such a system, students are rewarded for prompt withdrawals rather than for the messy, time-consuming work of questioning and creating.

Instead, AI should be positioned as a collaborative partner — a co-investor in learning, not just a bank teller. This requires rethinking the pedagogy:

AI prompts could invite students to generate original hypotheses rather than reproduce model answers.

Platforms could reward curiosity-driven exploration rather than quick completion rates.

Data ownership could be reimagined so students retain control over their intellectual deposits.

Conclusion

The metaphor of the banking model is more relevant than ever, but the teller has changed. Without a conscious shift towards participatory, critical, and human-centred AI use, we risk creating an educational economy that values speed and compliance over intellectual capital and democratic learning. The question is no longer whether AI will be integrated into education — it already has — but whether it will serve as a vault for storing facts or as an incubator for nurturing wisdom.

References

Freire, P. (1970). Pedagogy of the Oppressed. New York: Continuum.

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