# Qu'est que la littératie pour l'IA ?

Thierry Viéville

► To cite this version:

Thierry Viéville. Qu'est que la littératie pour l'IA?. APMEP 2025 - Journées Nationales de l'Association des Professeurs de Mathématiques de l'Enseignement Public, Oct 2025, Toulon (83000), France. hal-05314824

HAL Id: hal-05314824

https://inria.hal.science/hal-05314824v1

Submitted on 14 Oct 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# What is AI Literacy?

AI literacy represents the **technical knowledge, durable skills, and future-ready attitudes** required to thrive in a world influenced by AI. It enables learners to engage, create with, manage, and design AI, while critically evaluating its benefits, risks, and ethical implications.

## Competences

Each competence represents a learning expectation combining **Knowledge**, **Skills**, and **Attitudes**, illustrated through primary and secondary learning scenarios.

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## Attitudes

- Responsible

- Curious

- Innovative

- Adaptable

- Empathetic

## Skills

- Critical Thinking

- Creativity

- Computational Thinking

- Self and Social Awareness

- Collaboration

- Communication

- Problem Solving

## Knowledge

- Nature of AI

- AI Reflects Human Choice and Perspectives

- AI Reshapes Work and Human Roles

- AI's Capabilities and Limitations

- AI's Role in Society

## Why AI Literacy?

As young people begin using AI in more aspects of their lives, they need guidance to understand what it means, how it works, and how to use it responsibly. Though the majority of young people are already using AI, most say they learn about it outside of school. AI literacy provides a clear understanding of how AI technologies work and what their responsible use entails, so that learners can make safe and informed decisions.

# Four Domains of AI Literacy

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## Engaging with AI

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Engaging with AI in daily life involves using AI as a tool to access new content, information, or recommendations.

- **Recognize** Al's role and influence in different contexts.

- **Evaluate** whether AI outputs should be accepted, revised, or rejected.

- **Examine** how predictive AI systems provide recommendations that can inform and limit perspectives.

- **Explain** how Al could be used to amplify societal biases.

- **Describe** how Al systems consume energy and natural resources.

- **Analyze** how well the use of an Al system aligns with ethical principles and human values.

- **Connect** Al's social and ethical impacts to its technical capabilities and limitations.

## Creating with AI

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Creating with AI consists of collaborating with AI in a creative or problem-solving process.

- **Use** Al systems to explore new perspectives and approaches that build upon original ideas.

- **Visualize, prototype, and combine** ideas using different types of AI systems.

- **Collaborate** with generative AI systems to elicit feedback, refine results, and reflect on thought processes.

- **Analyze** how Al can safeguard or violate content authenticity and intellectual property.

- **Explain** how Al systems perform tasks using precise language that avoids anthropomorphism.

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## Managing AI

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Managing AI requires intentionally choosing how AI can support and enhance human work.

- **Decide** whether to use Al systems based on the nature of the task.

- **Decompose** a problem based on the capabilities and limitations of both Al systems and humans.

- **Direct** generative Al systems by providing specific instructions, appropriate context, and evaluation criteria.

- **Delegate** tasks to Al systems to appropriately automate or augment human workflows.

- **Develop and communicate** guidelines for using Al systems that align with human values, promote fairness, and prioritize transparency.

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## Designing AI

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Designing AI empowers learners to shape AI through hands-on exploration of the data used by AI models and engagement with AI design decisions.

- **Describe** how Al systems can be designed to support a solution to a community problem.

- **Compare** the capabilities and limitations of Al systems that follow algorithms created by humans with those that make predictions based on data.

- **Collect and curate** data that could be used to train an Al model by considering relevance, representation, and potential impact.

- **Evaluate** Al systems using defined criteria, expected outcomes, and user feedback.

- **Describe** an Al model's purpose, intended users, and its limitations.

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