| A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | AA | AB | AC | AD | AE | AF | AG | AH | AI | AJ | AK | AL | AM | AN | ||
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1 | Unit | Foundations | AI Safety | Individual lessons | |||||||||||||||||||||||||||||||||||||
2 | Lesson | Lesson 1 | Lesson 2 | Lesson 3 | Lesson 4 | Lesson 5 | Lesson 6 | Session 1 | Session 2 | Session 3 | AI and Ecosystems | LLMs | |||||||||||||||||||||||||||||
3 | Learning objective | Describe the difference between ‘data-driven’ and ‘rule-based’ approaches to application development | Name examples of AI applications | Outline some benefits and issues of using AI applications | Define machine learning’s relationship to artificial intelligence | Name the three common approaches to machine learning | Describe how classification can be solved using supervised learning | Describe the impact of data on the accuracy of a machine learning (ML) model | Explain the need for both training and test data | Explain how bias can influence the predictions generated by an ML model | Describe how decision trees are used to build a classification ML model | Describe how training data changes an ML model | Explain why ML is used to create decision trees | Describe the stages of the AI project lifecycle | Use a machine learning tool to import data and train a model | Test and examine the accuracy of a machine learning model | Evaluate an ML model | Produce a model card to explain an ML model | Recognise the range of opportunities that exist in AI-related careers | Explain the differences between rule-based and data-driven systems | Evaluate the ways they are sharing data that could be used in a data-driven system | Build a set of expectations of fairness, transparency and accountability in how an AI application uses their data | Describe different types of media that Generative AI tools can produce | Determine how Generative AI will affect the need to check information before sharing it | Build a set of expectations of fairness, accountability and transparency around AI content on a social platform | Choose AI tools they might want to use to help them complete tasks | Compose a list of their responsibilities when using AI tools | Build a set of expectations of fairness, accountability and transparency around AI tools available to them | Describe why artificial intelligence (AI) is a useful tool in helping to maintain biodiversity | Discuss some of the benefits and drawbacks of using AI | Describe the purpose of a large language model (LLM) | Recognise and discuss why the output of an LLM is not always trustworthy | Evaluate the appropriateness of an LLM for a range of authentic scenarios | 32 | |||||||
4 | UNESCO Aspects | Level | Competency block | Competency | Student competency | Curricula goal code | Curricula goals | ||||||||||||||||||||||||||||||||||
5 | Human-centred mindset | Understand | 4.1.1 | Human agency | Students are expected to be able to recognize that AI is human-led and that the decisions of the AI creators influence how AI systems impact human rights, human–AI interaction, and their own lives and societies. They are expected to understand the implications of protecting human agency throughout the design, provision and use of AI. Students will understand what it means for AI to be human-controlled, and what the consequences could be when that is not the case. | CG4.1.1.1 | Foster an understanding that AI is human-led | ||||||||||||||||||||||||||||||||||
6 | Human-centred mindset | Understand | 4.1.1 | Human agency | CG4.1.1.2 | Facilitate an understanding on the necessity of exercising sufficient human control over AI | |||||||||||||||||||||||||||||||||||
7 | Human-centred mindset | Understand | 4.1.1 | Human agency | CG4.1.1.3 | Nurture critical thinking on the dynamic relationship between human agency and machine agency | |||||||||||||||||||||||||||||||||||
8 | Human-centred mindset | Apply | 4.2.1 | Human accountability | Students are expected to be able to recognize that human accountabilities are the legal obligations of AI creators and AI service providers, and understand what human accountabilities they should assume during the design and use of AI. They should also foster an awareness that human. accountability is a legal and social responsibility when using AI to assist decisions on that affect humanity • Students are expected to be able to recognize that human accountabilities are the legal obligations of AI creators and AI service providers, and understand what human accountabilities they should assume during the design and use of AI. They should also foster an awareness that human accountability is a legal and social responsibility when using AI to assist decisions on that affect humanity | CG4.2.1.1 | Develop a view that human accountability is a legal obligation of AI creators and AI service providers | ||||||||||||||||||||||||||||||||||
9 | Human-centred mindset | Apply | 4.2.1 | Human accountability | CG4.2.1.2 | Generate the understanding that human accountability is a legal and social responsibility when using AI in making decisions about humanity | |||||||||||||||||||||||||||||||||||
10 | Human-centred mindset | Apply | 4.2.1 | Human accountability | CG4.2.1.3 | Nurture the personal attitude that human accountability requires personal competencies to steer the purposeful use of AI | |||||||||||||||||||||||||||||||||||
11 | Human-centred mindset | Create | 4.3.1 | AI society citizenship | Students are expected to be able to build critical views on the impact of AI on human societies and expand their human- centred values to promoting the design and use of AI for inclusive and sustainable development. They should be able to solidify their civic values and the sense of social responsibility as a citizen in an AI society. Students are also expected to be able to reinforce their open-minded attitude and lifelong curiosity about learning and using AI to support self-actualization in the AI era. | CG4.3.1.1 | Foster awareness of being a critical AI citizen | ||||||||||||||||||||||||||||||||||
12 | Human-centred mindset | Create | 4.3.1 | AI society citizenship | CG4.3.1.2 | Nurture personal and social responsibilities in AI societies | |||||||||||||||||||||||||||||||||||
13 | Human-centred mindset | Create | 4.3.1 | AI society citizenship | CG4.3.1.3 | Nurture the sense of self-actualization as an AI citizen and the lifelong learning attitude to AI | |||||||||||||||||||||||||||||||||||
14 | Ethics of AI | Understand | 4.1.2 | Embodied ethics | Students are expected to be able to develop a basic understanding of the ethical issues around AI, and the potential impact of AI on human rights, social justice, inclusion, equity and climate change within their local context and with regard to their personal lives. They will understand, and internalize the following key ethical principles, and will translate these in their reflective practices and uses of AI tools in their lives and learning: • Do no harm: Evaluating AI’s regulatory compliance and potential to infringe on human rights • Proportionality: Assessing AI’s benefits against risks and costs; evaluating context-appropriateness • Non-discrimination: Detecting biases and promoting inclusivity and sustainability (understanding AI’s environmental and societal impacts) • Human determination: Emphasizing human agency and accountability in AI use • Transparency: advocating for the rights of users to understand AI operations and decisions | CG4.1.2.1 | Illustrate dilemmas around AI and identify the main reasons behind ethical conflicts | ||||||||||||||||||||||||||||||||||
15 | Ethics of AI | Understand | 4.1.2 | Embodied ethics | CG4.1.2.2 | Facilitate scenario-based understandings of ethical principles on AI and their personal implications | |||||||||||||||||||||||||||||||||||
16 | Ethics of AI | Understand | 4.1.2 | Embodied ethics | CG4.1.2.3 | Guide the embodied reflection and internalization of ethical principles on AI | |||||||||||||||||||||||||||||||||||
17 | Ethics of AI | Apply | 4.2.2 | Safe and responsible use | Students are expected to be able to carry out responsible AI practices in compliance with ethical principles and locally applicable regulations. They are expected to be conscious of the risks of disclosing data privacy and take measures to ensure that their data are collected, used, shared, archived and deleted only with their deliberate and informed consent. They are also expected to be conscious of typical AI incidents and the specific risks of certain AI systems, and be able to protect their own safety and that of their peers when using AI. | CG4.2.2.1 | Foster self-awareness and habitual compliance with ethical principles for the responsible use of AI | ||||||||||||||||||||||||||||||||||
18 | Ethics of AI | Apply | 4.2.2 | Safe and responsible use | CG4.2.2.2 | Offer opportunities to reinforce self-discipline in the responsible use of AI | |||||||||||||||||||||||||||||||||||
19 | Ethics of AI | Apply | 4.2.2 | Safe and responsible use | CG4.2.2.3 | Deepen practical knowledge on the safe use of AI and awareness of locally applicable regulations | |||||||||||||||||||||||||||||||||||
20 | Ethics of AI | Create | 4.3.2 | Ethics by design | Students are expected to be able to adopt an ethics-by-design approach to the design, assessment and use of AI tools as well as the review and adaptation of AI regulations. Students are expected to be aware that the assessment and ratification of the intent of the AI design should start from the conceptualization stage and cover all steps of the AI life cycle. Student should be able to apply parameters to assess the compliance of an AI tool with ethical regulations and use an ethical matrix of multi-stakeholders to review AI regulations and inform adaptation. | CG4.3.2.1 | Build awareness and understanding on ‘ethics by design’ | ||||||||||||||||||||||||||||||||||
21 | Ethics of AI | Create | 4.3.2 | Ethics by design | CG4.3.2.2 | Develop a critical attitude to the ethics-by-design principles behind existing AI systems and algorithms | |||||||||||||||||||||||||||||||||||
22 | Ethics of AI | Create | 4.3.2 | Ethics by design | CG4.3.2.3 | Cultivating social responsibilities to uphold ‘ethics by design’ in regulations on AI | |||||||||||||||||||||||||||||||||||
23 | AI techniques and applications | Understand | 4.1.3 | AI foundations | Students are expected to develop basic knowledge, understanding and skills on AI, particularly with respect to data and algorithms, and understand the importance of the interdisciplinary foundational knowledge required for gradually deepening understanding of data and algorithms. Students should also be able to connect conceptual knowledge on AI with their activities in society and daily life, concretizing a human-centred mindset and ethical principles through an understanding of how AI works and how AI interacts with humans. | CG4.1.3.1 | Exemplify the definition and scope of AI | ||||||||||||||||||||||||||||||||||
24 | AI techniques and applications | Understand | 4.1.3 | AI foundations | CG4.1.3.2 | Develop conceptual knowledge on how AI is trained based on data | |||||||||||||||||||||||||||||||||||
25 | AI techniques and applications | Understand | 4.1.3 | AI foundations | CG4.1.3.3 | Foster open-minded thinking on AI and an interdisciplinary foundation for AI | |||||||||||||||||||||||||||||||||||
26 | AI techniques and applications | Understand | 4.1.3 | AI foundations | CG4.1.3.4 | Concretize human-centred considerations in the design and use of AI | |||||||||||||||||||||||||||||||||||
27 | AI techniques and applications | Apply | 4.2.3 | Application skills | Students are expected to be able to construct an age-appropriate knowledge structure on data, AI algorithms and programming, and acquire transferable application skills. Students are expected to be able to critically evaluate and leverage free and/ or open-source AI tools, programming libraries and datasets. | CG4.2.3.1 | Offer opportunities to strengthen knowledge and skills on data modelling, engineering and analysis | ||||||||||||||||||||||||||||||||||
28 | AI techniques and applications | Apply | 4.2.3 | Application skills | CG4.2.3.2 | Provide opportunities to acquire age-appropriate technical skills in AI programming | |||||||||||||||||||||||||||||||||||
29 | AI techniques and applications | Apply | 4.2.3 | Application skills | CG4.2.3.3 | Encourage students to develop analytical and synthesis skills to leverage open- source datasets and AI tools | |||||||||||||||||||||||||||||||||||
30 | AI techniques and applications | Create | 4.3.3 | Creating AI tools | Students are expected to be able to deepen and apply knowledge and skills on data and algorithms to customize existing AI toolkits to create task-based AI tools. Students are expected to integrate their human-centred mindset and ethical considerations into the assessment of the existing AI resources and the test of self created AI tools. They are also expected to foster social and emotional skills needed to engage in creation with AI including adaptivity, complex communication and teamwork skills. | CG4.3.3.1 | Challenge and enable advanced skills to develop task-based AI tools | ||||||||||||||||||||||||||||||||||
31 | AI techniques and applications | Create | 4.3.3 | Creating AI tools | CG4.3.3.2 | Enhance students’ creativity in applying AI knowledge and skills to customize AI toolkits and coding | |||||||||||||||||||||||||||||||||||
32 | AI techniques and applications | Create | 4.3.3 | Creating AI tools | CG4.3.3.3 | Equip students with skills to test and optimize their self-crafted AI tools | |||||||||||||||||||||||||||||||||||
33 | AI system design | Understand | 4.1.4 | Problem scoping | Students are expected to be able to understand the importance of ‘AI problem scoping’ as the starting point for AI innovation. They are expected to be able to examine whether AI should be used in certain situations from legal, ethical and logical perspectives; students are able to define the boundaries, goals and constraints of a problem before attempting to train an AI model to solve it; students are also expected to acquire the knowledge and project-planning skills needed in order to conceptualize and construct an AI system, including by assessing the appropriateness of different AI techniques, defining the need for data, and devising test and feedback metrics | CG4.1.4.1 | Scaffold critical thinking skills on when AI should not be used | ||||||||||||||||||||||||||||||||||
34 | AI system design | Understand | 4.1.4 | Problem scoping | CG4.1.4.2 | Support the acquisition and reinforcement of skills in scoping a problem to be solved by an AI system | |||||||||||||||||||||||||||||||||||
35 | AI system design | Understand | 4.1.4 | Problem scoping | CG4.1.4.3 | Develop skills on assessing AI systems’ need for data, algorithms and computing resources | |||||||||||||||||||||||||||||||||||
36 | AI system design | Apply | 4.2.4 | Architecture design | Students are expected to be able to cultivate basic methodological knowledge and technical skills to configure a scalable, maintainable and reusable architecture for an AI system covering layers of data, algorithms, models and application interfaces. Students are expected to develop the interdisciplinary skills necessary to leverage datasets, programming tools and computational resources to construct a prototype AI system. This includes the expectation that they apply deepened human-centred values and ethical principles in their configuration, construction and optimization. | CG4.2.4.1 | Scaffold the acquisition of methodological knowledge and technical skills on AI architecture: | ||||||||||||||||||||||||||||||||||
37 | AI system design | Apply | 4.2.4 | Architecture design | CG4.2.4.2 | Support the preparation of advanced technical skills and project management competencies needed by AI system building | |||||||||||||||||||||||||||||||||||
38 | AI system design | Create | 4.3.4 | Iteration and feedback | Students are expected to enhance and apply their interdisciplinary knowledge and practical methods to evaluate the humanistic appropriateness and methodological robustness of an AI model and its impact on individual users, societies and the environment. They should be able to acquire age-appropriate technical skills to improve the quality of datasets, reconfigure algorithms and enhance architectures in response to results of tests and feedback. They should be able to apply human-centred mindset and ethical principles in simulating decision-making on when an AI system should be shut down and how its negative impact can be mitigated. They are also be expected to cultivate their identities as co-creators in the larger AI community | CG4.4.4.1 | Develop the skills to critique AI systems | ||||||||||||||||||||||||||||||||||
39 | AI system design | Create | 4.3.4 | Iteration and feedback | CG4.4.3.2 | Support the building of technical skills and social responsibilities in optimizing, reconfiguring or shutting down an AI system | |||||||||||||||||||||||||||||||||||
40 | AI system design | Create | 4.3.4 | Iteration and feedback | CG4.4.4.2 | Foster students’self-identities as co-creators in the AI era: | |||||||||||||||||||||||||||||||||||
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