Competency model of the AI Curriculum
AI literacy is a set of competencies that enable individuals to function responsibly in a society shaped by artificial intelligence. A person with AI literacy should understand the basic principles of this technology, be able to critically evaluate both its positive and negative impacts, and use AI applications effectively. In a society undergoing rapid technological change, the goal of elementary and secondary schools is to help students develop the following competencies.
Knowledge about AI
Identify which systems (used in everyday life) are based on artificial intelligence technologies.
Explaining the difference between the algorithmic approach and machine learning.
In the algorithmic approach, the problem is first defined, then a solution is designed as an algorithm, which is implemented step by step and tested.
In the machine learning approach, the problem is also described first. Then data is collected, cleaned, processed, and annotated. A chosen model is trained on this data, tested, and often further improved.
How AI systems work
- Data: The role of data in AI, how it’s collected, processed, and used to train models.
- Big data: Large and diverse datasets requiring specific tools and methods to process.
- Data collection devices: Sensors, cameras, microphones, and other tools that provide input data for AI systems.
- Machine learning: Techniques used to train models for classification and prediction tasks.
- Bias: The risk of skewed results due to biased data selection, annotation, or interpretation of AI outputs.
AI applications
- Generative AI: Systems that create new content (e.g., text, images, audio, video, or 3D objects).
- Computer vision: Methods that allow machines to “see” and interpret visual input (e.g., face or object recognition, image analysis).
- Natural language processing (NLP): Technologies for understanding and generating human language (e.g., translation, chatbots, sentiment analysis).
- Recommendation systems: Tools that suggest products, content, or services based on user behavior and preferences (e.g., social media, search engines, streaming platforms).
- Ambient intelligence: Connecting AI with the Internet of Things.
- Robotics
AI in context
- AI ethics: Guidelines for responsible AI use, including data ethics and ethical decision-making.
- Synthetic reality: Digital manipulation, media generation, and how agents or environments (like deepfakes or synthetic media) shape online space.
AI skills
Collect, clean, process, and annotate data. Build simple AI-powered programs, test them, and gradually improve them.
Use appropriate AI tools to solve problems and combine them in meaningful ways. Apply AI tools in real projects or applications, such as task automation, content personalization, or data analysis.
Approach to AI
Distinguish between ethical and unethical data practices across the stages of creating and using AI systems. Ethical handling includes transparent data collection with consent and privacy protection, while unethical practices involve illegal collection, data manipulation, or ignoring imbalances, which can lead to bias.
Clarify that technical systems are also sociotechnical — they’re not neutral, as they serve political, promotional, or other agendas and are shaped by actors with diverse values and goals. Evaluate how AI influences decision-making and what ethical dilemmas may arise from its use.
Critically assess the positive and negative impacts of AI on individuals, society, and the environment. Form an opinion on specific AI uses, such as facial recognition or recommendation systems. Describe consequences such as job automation, effects on privacy, safety, or equal opportunities.