Artificial Intelligence (AI) has recently transformed into a comprehensive technological, scientific, and business ecosystem, extending beyond impressive advances in specific applications to become a disruptive and highly pervasive field. Among the sectors impacted, education stands out prominently.

Generative AI models have been solidified in recent years, driven by foundational models, with applications in areas such as natural languages (LLMs), artificial vision, and speech recognition and synthesis.

In education, a primary application focus has been the use of conversational interaction models to access LLMs through a prompt engineering approach, supporting various educational tasks. This method positions Generative AI as a vital tool for teachers in their daily multi-tasking activities.

Leveraging both the extensive historical development of AI and the latest theoretical and applied advancements, along with a comprehensive and interdisciplinary perspective, AI presents a significant opportunity for innovative teaching methods. This paper outlines the guidelines for this approach, explores the opportunities, and addresses key challenges.

The rationale for this approach is based on the following consideration: if we have numerous computer tools that can perform complex tasks in almost every field of knowledge (mathematics, physics, biology, etc.), even with historical, legal, or artistic knowledge bases, why do we continue teaching the principles, techniques, or concepts of these disciplines? For instance, if a simple computer system can solve a quadratic equation, why continue teaching the traditional formula?

Computational thinking is described as the thought processes involved in formulating problems so their solutions can be represented algorithmically, with abstraction and generalization as key pillars.

A recent interdisciplinary study, involving ethnographic-anthropological analysis with students of scientific-technical degrees, revealed that understanding the techniques behind AI models and approaches transformed their perceptions of language, intelligence, emergence, and reasoning, normalizing and demystifying the often mysterious nature of AI.

Building on initiatives led by organizations like UNESCO (Beijing Consensus on Artificial Intelligence and Education 2019; Education in the Age of Artificial Intelligence 2023; Guidance for Generative AI in Education and Research 2023) and the AI for K-12 initiative (AI4K12), this work defines the AIThinking approach, outlines guidelines for curricular framework development, and addresses both educational and technological challenges, as well as the opportunities this approach offers for the educational process.

In this context, we must consider that AI focuses on aspects such as knowledge representation, reasoning process modeling, intelligent behavior simulation, and learning strategy implementation.

AIThinking proposes a framework for exploration, analysis, conception, modeling, and problem-solving that unifies a human-centered approach, incorporating both critical and computational thinking, and integrates teaching in an inter- and multidisciplinary context.

Keywords: AI, Education, Technology, AIThinking, Human-centered AI, Computational thinking, Critical thinking.