UNESCO AI Competency Framework for Teachers
A brief overview of the organization's lastest AI-related release
Introduction
AI technologies offer both significant opportunities and challenges for educators. While they can enhance teaching, learning, and school management, they also risk diminishing human agency and reducing teaching and learning to automated tasks. As explained by the UNESCO in a report released just a few days ago during Digital Learning Week, an AI competency framework is therefore essential to ensure teachers are equipped to play the important role they have in facilitating human / AI interactions and steering them in the right direction.
“Teachers are the key mediators in ensuring adequate balance in the evolving relationship between humans and technology.”
Key principles
The UNESCO’s “human-centered approach” to AI is grounded in four principles:
Empowering teachers’ human-accountable use of AI: AI tools should not replace the legitimate accountability of teachers in education.
Promoting inclusivity: Teachers need to ensure AI is used in an inclusive manner by and for all.
Recognizing users’ rights to question the explainability of AI tools: Teachers should be equipped to understand and critically evaluate AI tools.
Understanding and monitoring the human-controlled impact of AI: Teachers should be able to harness the benefits of AI while controlling its possible adverse impacts of student learning and well-being.
In addition, institutions should:
Protect teachers’ rights as they iteratively redefine their roles and necessary competencies
Validate the safety and trustworthiness of AI systems in education
Ensure teachers have the opportunity to develop AI competencies, including:
Progressive acquisition and continuous adaptation
Cycles of lesson design, implementation and reflection
Ongoing coaching and support
Sufficient time allocation and incentives
Adapting curriculum and assessment systems to allow for experimentation and evolution
Competency Aspects
The AI CFT contains 5 competency aspects :
Human-centred mindset : This aspect emphasizes prioritizing human rights, human flourishing, and critical evaluation of AI’s benefits and risks while ensuring human agency and accountability.
Ethics of AI: This aspect encompasses ethical values, principles, regulations, and practical rules surrounding AI use in education.
AI foundations and applications: This aspect covers the conceptual knowledge and operational skills needed to select, apply, and customize AI tools for student-centered learning environments.
AI pedagogy: This aspect focuses on competencies for purposeful AI integration into pedagogy. It encompasses validating and selecting appropriate AI tools, integrating them with teaching methods, and supporting course preparation, teaching, learning, and assessment.
AI for professional development: This aspect addresses the competencies needed to use AI for continuous professional learning and development. It includes leveraging AI for self-assessment, personalized learning pathways, and collaborative professional development.
Progression Levels and Specifications
Each of the 5 competency aspects evolves across 3 progression levels, which the AI CFT further specifies through:
Curricular goals: “Teacher training or support programmes should…”
Learning objectives: “Teachers can…”
Contextual activities: “Teachers can demonstrate the following attitudinal or behavioral changes…”
The original report contains detailed tables, but here is a broad overview with:
The Relevant Competency Aspect for Each Level
The corresponding contextual activities
Acquire (Basic AI literacy)
Human Agency
Unpack hype around AI
Understand why some AI tools should be banned
Spotlight risks
Know basic do’s and don’ts
Ethical Principles
Perspective-taking in ethical dilemmas
Knowledge-mapping of ethical principles
Personal observation of local regulations
Biases of AI tools
Basic AI techniques and applications
Conceptual mapping of how AI works
Extension and enhancement of skills
‘Navigation compass’ for selection of AI tools
Collection of appropriate AI tools
AI-assisted teaching
Starting from basic teaching needs
Learning by the iterative cycle of ‘design–implementation–reflection’
Evaluating effectiveness against needs
Enabling lifelong professional learning
Awareness of teachers’ basic rights and obligations in the AI era
Self-assessment of readiness for teaching in the AI era
Human-directed use of AI to open professional learning horizons
Deepen (Mastery Level)
Human accountability
Human accountability in AI-assisted decision loops is a legal obligation
Teachers’ accountability and rights cannot be usurped by AI
Teachers’ accountability is a human assurance for ethical and effective uses of AI in education
Safe and responsible use
Personal AI safety tracker
Whitelist the personal collections of AI tools for education
Iteratively update list of dos and don’ts
Application skills
Skillful uses of AI tools in schools
Visualized ‘know-how’ on typical categories of AI tools
Facilitating students to learn about data, algorithms and coding
Informed whistleblowing in ethics by design
AI-pedagogy integration
Mapping of AI tools and application skills
Insights into pedagogical assumptions behind AI tools
Designing and facilitating students’ use of AI for higher-order thinking and
social-emotional learning
Human-accountable AI-assisted assessments
AI to enhance organizational learning
Autonomous upskilling and peer coaching
Using data analytics for self-regulated professional development
Generative AI simulations for professional development
Human-controlled uses of AI for collaborative professional development
Create (Expert Level)
Social responsibility
Teachers’ voices on human and planetary well-being in the AI era
Reflection on and promotion of human-centric social relations and social
cohesion
Rights, obligations, and responsibilities of citizenship in the era of AI
Co-creating ethical rules
Localized global view on the social impact of AI
Spotlighting ethical gaps in users’ guidance
Master teachers as advocates of AI ethics
Co-designing ethical prototypes of AI tools for education
Creating with AI
Driving the design of AI tools for inclusion
Promoting the co-creation of AI tools to support climate-friendly actions
Coordinating the building and use of repositories of educational AI tools
AI-enhanced pedagogical innovation
Guiding the pedagogical uses of AI while leveraging AI to open new pedagogical horizons
Engineering triangular interactions between teachers, students and AI
AI empowering students with special needs
Human–AI hybrid approach to development of curricular resources
AI to support professional transformation
Human–AI hybrid coach for teachers
AI-enhanced design of training programmes
Communities for the co-creation of AI tools, pedagogical innovations, or ethical rules
Implementation Strategies
Finally, the UNESCO report outlines strategies for translating the AI CFT into action. While this includes guidance for both school leaders and policy-makers, I will focus on the former.
Ensure trustworthy AI tools for education
School should implement independent mechanisms for validating AI systems before deployment, particularly those targeting younger children.
Build enabling policies and conditions for the use of AI in education
These policies should:
Prioritize human-centered AI: Conduct cost-benefit analyses to ensure AI's value outweighs its risks and prioritizes human agency and creativity in education.
Support and motivate teachers: Integrate AI competencies into professional development frameworks, provide adequate training, recognize innovative practices, and mitigate negative impacts of AI on workload.
Ensure inclusive access: Address economic and structural barriers by providing free or affordable access to validated and trustworthy AI tools and upgrading digital infrastructure.
Formulate and adopt local AI competency frameworks for teachers
While the AI CFT serves as a blueprint, schools should develop localized frameworks tailored to their specific contexts. This involves:
Assessing AI readiness: Evaluating the availability of AI tools and existing competency levels among teachers.
Identifying competency gaps: Analyzing discrepancies between desired competencies and those addressed in existing training programs.
Articulating localized frameworks: Defining key aspects and mastery levels aligned with local digital readiness and professional development frameworks.
Design training and support programmes on AI competencies
As is clear from the Specifications, the AI CFT offers a practical framework for planning teacher AI training. This involves:
Tailoring training to the AI CFT: Using the curricular goals and learning objectives outlined in Chapter 4 to frame training content.
Addressing all career stages: providing adapted, ongoing support, including peer mentoring and communities of practice.
Ensuring flexibility and contextual relevance: Customizing programs to the specific needs and contexts of specific educational environments and teacher expertise levels.
Develop contextual performance-based assessment tools
The AI CFT can also guide the development of assessment tools for evaluating teachers' AI competencies. This involves:
Designing relevant assessment methods: Creating assessment items aligned with the adapted objectives and drawing on the contextual activities.
Specifying grading criteria: Clearly defining criteria for assessing competency based on the dapted learning objectives.
Conclusion
Released just a few days ago by the UNESCO, the AI CFT is the temporary fixed version of an instrument that is the product of many contributions, iterations and rounds of feedback. The goal of this article was not to discuss its merits, but simply to propose a broad overview. As mentioned by the UNESCO, the framework is open to adaptations, which will hopefully prompt meaningful discussions and encourage both AI adoption and protection in local school contexts - all while giving educators a common model and language worldwide.