AI Curriculum Competency Model
AI literacy encompasses a set of competencies that enable individuals to function effectively in an AI-driven society. A person with AI literacy should understand the fundamental principles of AI, critically evaluate its benefits and risks, and effectively utilize AI applications. As technology evolves rapidly, the goal of primary and secondary education is to develop the following key competencies in students.
AI Knowledge
Recognizing which everyday systems are powered by artificial intelligence.
Understanding the difference between traditional programming (rule-based algorithms) and machine learning (data-driven models).
Algorithmic Approach
+ Define the problem
+ Design an algorithmic solution
+ Implement step-by-step
+ Compile, run, and test the program
Machine Learning Approach
+ Describe the problem and collect data
+ Clean, process, and annotate data
+ Train an AI model
+ Test and refine both the model and dataset
How AI systems work
- AI Systems & Data–The role of data in AI, data collection, processing, and model training.
- Big Data–Large, diverse datasets requiring specialized processing methods.
- Data Collection Devices – Sensors, cameras, microphones, and other inputs for AI systems.
- Machine Learning—Algorithms for classification and predictive tasks.
- Bias in AI–Risks of skewed data selection, annotation, and interpretation affecting model accuracy.
AI applications
- Generative AI–AI models capable of creating text, images, audio, video, and 3D content.
- Computer Vision–AI-powered image and facial recognition, object detection, and visual data analysis.
- Natural Language Processing (NLP)–AI-driven language comprehension, translation, chatbots, and sentiment analysis.
- Recommender Systems–AI-powered personalized content recommendations (e.g., social media, search engines, streaming platforms).
- Ambient Intelligence–The integration of AI with IoT to create responsive environments.
- Robotics–AI-enhanced autonomous systems and automation.
AI in context
- AI Ethics–Principles for responsible AI use, data ethics, and ethical decision-making.
- Synthetic Reality–AI-generated media, deepfakes, and the transformation of online spaces.
AI skills
Collecting, cleaning, modifying, and annotating data and developing simple AI-powered programs, testing, and improving models.
Selecting the right AI tools for problem-solving and combining AI tools for automation, content personalization, and data analysis.
Attitude Toward AI
Understanding ethical vs. unethical data handling, including privacy protection, transparent data collection, and avoiding manipulation or bias.
Recognizing that AI systems are not neutral—they reflect the values and interests of those who create them and evaluating AI’s role in decision-making and the ethical dilemmas it presents.
Critically evaluating AI’s social, economic, and environmental effects and discussing AI applications such as facial recognition, job automation, privacy, security, and equal opportunity.