2025年赋能人工智能时代的学习者_中小学人工智能素养框架报告（英文版）_42页_3mb

报告摘要

AI Literacy Framework for Primary and Secondary Education

This document outlines the AILit Framework, a joint initiative by the European Commission and OECD, with support from Code.org and international experts. Its goal is to define AI literacy—a combination of knowledge, skills, and attitudes—and integrate it into education to prepare learners for the AI era.

Key Points

Purpose: Equip learners to critically evaluate and responsibly use AI in daily life, education, and communities.
Intended Audience: Teachers, education leaders, policymakers, and learning designers in primary/secondary education.
Draft Status: A review version (May 2025), to be finalized in 2026 with public feedback through platforms like teachai.org/ailiteracy/review.

Framework Structure

Four Domains of AI Literacy:
- Engaging with AI: Use AI as a tool, evaluating its accuracy and ethical implications.
- Creating with AI: Collaborate with AI to generate ideas, considering ownership and attribution.
- Managing AI: Delegate tasks intentionally to enhance human work and prioritize ethical use.
- Designing AI: Understand how AI systems work and consider their societal and ethical impacts.
Foundational Components:
- Knowledge: Technical understanding of AI, including biases, limitations, and societal roles.
- Skills: Critical thinking, creativity, computational thinking, and collaborative abilities in an AI context.
- Attitudes: Responsible, curious, innovative, adaptable, and empathetic engagement with AI.
Interdisciplinary Approach: AI literacy is integrated into subjects like computer science, ethics, data science, and design thinking.
Supporting Elements:
- Stakeholder Dialogue: Educators and policymakers are encouraged to shape the framework’s evolution.
- Future Implementation: The framework aligns with evolving policies like the EU AI Act and will support initiatives for curriculum, assessment, and professional learning.

Background and Context

The framework builds on existing digital and AI-related frameworks (e.g., DigComp, UNESCO’s AI Competencies, OECD’s insights), aiming to address current barriers like the lack of shared AI education priorities. It emphasizes ethical AI literacy to counter biases, misinformation, and skills gaps.