Abstract

Artificial Intelligence (AI) is transforming every aspect of modern life, yet its rapid evolution has created misconceptions among secondary students and even educators, many of whom associate AI solely with generative tools like ChatGPT or image generators. This article argues that teaching AI requires a broader, interdisciplinary perspective. AI is not just computer science; it is a culmination of philosophy, mathematics, biology, linguistics, engineering, and social sciences. By understanding the historical, philosophical, and scientific foundations of AI, learners can better appreciate the human role in shaping intelligent systems and navigate the ethical challenges of collaborating with machines.

Keywords

Artificial Intelligence, Philosophy of Intelligence, Interdisciplinary Learning, Modeling, Ethical AI, Secondary Education, Generative AI

Introduction

As AI becomes increasingly present in classrooms and daily life, educators face a critical question: How do we introduce AI in a meaningful and responsible way to secondary students? Many learners believe AI began with ChatGPT, unaware of the centuries of intellectual effort that shaped today’s technologies.

AI is not a standalone field. It is a historical journey that began with philosophers asking, What is intelligence? and continues today with scientists training large-scale generative models. To teach AI effectively, we must uncover the roots of this journey and highlight the essential role of every academic domain, not just computer science.

Why We Must Teach AI Beyond “Generative AI”

Focusing exclusively on generative tools risks reducing AI to a collection of tricks. Students must instead explore AI in its general sense:

• What does it mean for a machine to “think”?
• How do we model human reasoning?
• Why do algorithms and neural networks behave the way they do?

Understanding these foundations is essential if we expect learners to use AI responsibly, creatively, and ethically.

The Interdisciplinary Nature of AI: A Message for Teachers and Students

1. AI Was Not Born in a Computer Lab

   Long before programmers existed, philosophers grappled with defining intelligence, logic, consciousness, and reasoning. Their debates laid the groundwork for formal logic, which later shaped modern algorithms.

2. Mathematics Transformed Ideas into Models

   Pascal’s mechanical calculator, Boolean logic, and symbolic reasoning allowed thinkers to simulate aspects of human cognition, moving from abstract reflections to concrete computational models and machine learning.

3. Biology Revealed the Blueprint of Learning

   When biologists uncovered how neurons transmit information, they unknowingly ignited the creation of artificial neural networks, the foundation of modern deep learning.

4. Linguistics Shaped the Language Machines Understand

   From Markov chains to today's transformers, language modeling emerged from experts who understood the deep structure of human language, not from computer scientists alone.

5. Social Sciences and Philosophy Guide Ethical Boundaries

   Human-machine collaboration requires an understanding of psychology, sociology, ethics, responsibility, and the consequences of technological deviation.

This is why every domain matters.
AI is not a “tech subject.” AI is the result of all disciplines evolving together.

Modeling: The Bridge Between Ideas and Reality

A core theme students must understand is the role of modeling. Every innovation, from Pascal’s calculator to GPT, began as an idea that needed structure, equations, representation, and simulation. Modeling is what turns:

• Philosophy into logic
• Biology into neural networks
• Linguistics into NLP systems
• Human communication into machine interaction

Teaching modeling empowers learners to transform their own abstract ideas into actionable innovations.

The Human–Machine Partnership: A Challenge of Our Time

AI is no longer a distant concept. Secondary students already collaborate daily with intelligent systems, sometimes without realizing it. This new reality demands two responsibilities:

1. Respect the human role: Machines do not replace human purpose, ethics, creativity, or judgment.
2. Understand the limits of machines: AI does not “know”; it predicts, processes, and models.

Preparing students for the future means helping them live this duality:
We advance with machines, but we remain the authors of meaning, responsibility, and progress.

Conclusion

Introducing AI to secondary students requires more than technical tutorials. It demands a holistic narrative, a story that connects philosophy, science, mathematics, language, biology, modeling, and ethics. When students understand AI as a human-center journey rather than a mysterious technology, they gain the ability to guide its future rather than simply consume it.

AI literacy is not about producing programmers. It is about producing thinkers, creators, and responsible citizens ready to collaborate with intelligent systems, while never losing sight of the humanity that made AI possible.