The original version of the SAIL framework was based on the results of a Delphi study. After we made it publicly available for use and feedback, and began applying it in different ways to our own practice, we identified several areas for improvement. The main issue was the lack of clear pathways through the levels for specific competencies. To remedy this, we introduced key ideas in each category that ran across all four levels, making it much easier to see how a given competency could be scaffolded over time.
The levels were also modified to make it more explicit that the highest level was not part of AI literacy, but what comes beyond it, so rather than being numbered, it is now ‘AI++’. Other changes were that the first level was renamed ‘Understand and Explore’ AI instead of ‘Know and Understand AI’ to make it clear that at this level there was active engagement with AI tools, and the second level was renamed ‘Apply and Integrate AI’ instead of ‘Use and Apply AI’ to indicate a more developed level of use.
Complete table of competencies
| Key Idea | Level 1 – Understand and Explore AI | Level 2 – Apply and Integrate AI | Level 3 – Evaluate and Create AI | AI++ Transform and Develop AI (Beyond AI Literacy) |
| Concepts | ||||
| The Impacts of AI | ||||
| 1. People, Organisations, and AI | Recognise critical issues in the relationship between people and AI (bias, hallucinations, uncritical use). | Discuss how AI systems address the needs of people and organisations. | Discuss the role of people and organisations in the development and deployment of AI. | Design and implement AI strategies to lead ethical governance and contribute to shaping AI policy/standards. |
| 2. Engagement with AI | Identify how people are using AI in daily life; give examples of sector impacts. | Discuss the opportunities and risks of AI adoption in specific contexts and how access to AI technologies impacts participation in education, work, and civic life. | Evaluate current and potential uses/harms across disciplines; Analyse how global inequities shape who builds AI and who bears the risks. | Explore implications of future AI technologies and state-of-the-art developments. |
| 3. AI as a Technology | Explain basic concepts of AI and distinguish AI from other technologies. | Understand how algorithms work and how they are used in AI tools and applications. | Evaluate AI models, including fairness across populations; create design principles for AI in context. | Analyse the impact of advanced computing concepts and future technical directions. |
| 4. AI in Context | Give examples of how AI has impacted different sectors. | Demonstrate an understanding of AI’s role in a specific situation | Demonstrate an understanding of the role of AI across interdisciplinary fields. | Demonstrate an understanding of the state-of-the-art in AI and its future directions, addressing the benefits and potential risks. |
| 5. Indigenous Perspectives on AI | Recognise Indigenous knowledge, worldviews and values in relation to technology. | Describe how AI can support indigenous aspirations in education, health, and cultural preservation. | Critically evaluate AI systems for alignment with Indigenous data sovereignty and Pacific cultural frameworks. | Co-develop AI initiatives in partnership with Indigenous communities, embedding relevant cultural principles in governance and design. |
| What AI Is and How It Works | ||||
| 6. AI Terms and Features | Define and recognise common AI terms and features. | Apply terms to explain how AI models are trained, including steps such as input, training, and outputs. | Evaluate how deep learning and other architectures shape model capabilities and limitations | Evaluate how deep learning and other architectures shape model capabilities and limitations |
| 7. Types of AI | Identify what is, and is not, AI; describe basic categories (rule-based vs. machine learning, generative vs. discriminative). | Compare the main features of different AI approaches | Evaluate the strengths, limitations, and application contexts of different AI systems. | Design or adapt AI solutions that leverage appropriate system types for specific domains or problems. |
| 8. AI and Data | Explain the role of data in AI systems, using simple examples. | Identify and discuss different data sources, including issues of quality, bias, and representativeness. | Evaluate how AI systems address problems, how their algorithms are developed, and how they learn patterns from large datasets. | Apply advanced data practices in the development or adaptation of AI systems. |
| Application of AI and Technical Skills | ||||
| Cognitive Skills | ||||
| 9. Understanding AI in Use | Identify features of AI and describe their applications and interactions | Explain how AI influences tasks, work, and systems. | Evaluate how AI adoption may impact workers differently across industries, focusing on the benefits and mitigating limitations, supporting equity and redistribution of benefits. | Design and evaluate end-to-end AI processes (from data to lifecycle management). |
| 10. Critical Application of AI | Recognise situations where using AI may or may not be appropriate. | Discuss how AI can support decision-making and problem-solving | Evaluate the suitability and usefulness of AI technologies for tasks. Evaluate the suitability of AI technologies, considering their potential to either support or disadvantage indigenous peoples. | Explore and critique emerging research directions in AI. |
| 11. AI Transparency | Demonstrate awareness of AI inaccuracies and simple checking strategies. | Discuss transparency and explainability in AI, and why they matter. | Evaluate approaches to enhance transparency, explainability, and accountability in AI. | Apply ethical considerations such as transparency, explainability, and fairness to AI practice/governance. |
| 12. Associated Literacies and Learning | Integrate AI literacy into broader digital literacies, considering how equity issues in AI literacy can be supported (recognising differences in access to technology, skills, and learning opportunities). | Demonstrate computational thinking skills relevant to AI | Demonstrate data literacy, including privacy, management, and governance. | Sustain continual learning in ethical, societal, and technological aspects of AI. |
| 13. AI in Research | Apply and evaluate how AI can support simple research practices (such as literature reviews). | Explore and discuss AI research outputs and implications. | Learn and apply new AI concepts, tools, and techniques independently. | Contribute to AI research through exploration, hypothesis generation, and scholarly engagement, ensuring knowledge creation respects cultural values. |
| Applied Skills | ||||
| 14. Selecting and Applying AI | Select and apply simple AI tools for specific tasks. | Determine which AI methods/tools best fit different problems or contexts.Determine which AI methods/tools best fit different problems or contexts. | Evaluate and implement creative approaches to AI applications across contexts. | Design and deliver AI projects with measurable outcomes, analysing and reporting findings |
| 15. Goal-based AI Application and Development | Apply AI tools to achieve personal, learning, or work goals. | Apply AI tools across different fields of study or disciplines and create simple AI applications that promote equitable outcomes. | Develop AI projects using scripts, tools, and libraries. | Build real-world AI applications leveraging advanced programming and AI techniques. |
| 16. Using and Creating AI Tools | Use AI tools to support simple tasks and reflect on their outputs | Use, adapt and/or combine AI tools to create basic applications. | Apply the steps in AI model development (training, testing, validation, deployment). | Design, implement, fine-tune, and troubleshoot advanced AI models using frameworks |
| 17. AI in Projects | Recognise examples of AI projects and their purposes. | Contribute to simple AI projects using basic tools or datasets. | Assess how human-centred and culturally grounded design principles affect AI projects. | Manage and lead AI projects, collaborating with teams and applying coding/software expertise. Embed Indigenous perspectives, working in partnership with communities. |
| 18. AI Technologies | Recognise different AI technologies (ML, NLP, CV, etc.). | Explain core machine learning approaches and their applications. | Evaluate how algorithms and data underpin approaches to AI, and their suitability. | Apply AI frameworks and principles to complex problem-solving. |
| 19. Data in AI Applications | Recognise that AI systems depend on data and identify examples of data used in everyday AI applications. | Explain how datasets (including training and testing sets) are used and transformed to create AI models, and how they must respect Indigenous data sovereignty and cultural protocols. | Process and manage data for AI applications using appropriate tools (e.g., cleaning, preparation, validation). Evaluate data practices for equity and inclusivity; ensure marginalised groups are not systematically under- or mis-represented. | Design and implement data pipelines, including integration, feature engineering, and optimisation for AI systems. Implement data governance practices that uphold Indigenous ownership, control, access, and possession (OCAP) principles. |
| AI Digital Citizenship | ||||
| Social, Cultural, and Ethical Issues | ||||
| 20. AI and Society | Recognise potential benefits and challenges of AI in society. | Explore the impact of AI on societal norms, work, creativity, and participation | Critically analyse approaches to mitigate societal/ethical risks. | Anticipate future AI directions and lead inclusive AI design that embeds Indigenous worldviews, ensuring culturally sustaining and just outcomes. |
| 21. Ethical Issues in AI | Identify common ethical concerns in AI (e.g., bias, fairness, transparency). | Provide examples of ethical issues across AI use cases, recognising the role of Te Tiriti o Waitangi and Pacific frameworks in shaping ethical AI use | Evaluate ethical challenges in AI design and implementation | Apply and test principles-based frameworks in practice |
| 22. AI and Culture | Explain AI’s relationship with culture and values. | Explore the impact of AI on Indigenous and disadvantaged groups | Analyse how AI design decisions may reinforce or disrupt structural inequities (e.g., racism, sexism, class bias). | Lead inclusive AI development that respects cultural values. |
| 23. AI Bias | Explain how bias occurs in AI systems. | Evaluate how data quality and sources shape bias in models | Analyse the impacts of bias and propose strategies for diverse datasets. | Demonstrate and critique deliberate bias to raise ethical awareness |
| 24. AI Policy | Recognise that AI requires governance and oversight.v | Describe examples of AI policies/regulations in practice | Discuss the role of AI policy and governance, including how it reflects obligations to Indigenous rights (such as those under Te Tiriti) | Contribute to policy and guideline development for ethical AI and respect Indigenous sovereignty and minority rights |
| 25. AI and Equity | Recognise that AI can create unequal impacts for different groups. | Explore how AI access and use vary across socio-economic, cultural, and geographic contexts. | Critically evaluate AI systems for equity, fairness, and accessibility. | Lead AI initiatives that promote equity, social justice, and inclusive designLead AI initiatives that promote equity, social justice, and inclusive design |
| Risks and Mitigations | ||||
| 26. Risks of AI Systems | Identify risks presented by AI systems (e.g., privacy, security, fraud, misuse). | Assess risks related to data use in AI, including accuracy, relevance, storage, and potential misuse. | Evaluate current risks of AI implementation, including human–AI interaction, intellectual property, and societal impacts | Anticipate and analyse future risks of AI (e.g., advanced cybersecurity threats, malicious use), balancing benefits against potential harms. |
| 27. Mitigating the Risks of AI | Recognise and describe simple ways to mitigate risks presented by AI systems (e.g., safe passwords, privacy settings). | Apply strategies to protect personal rights and privacy when interacting with AI (e.g., consent, data-sharing policies).v | Implement strategies to enhance safety, security, and reliability of AI systems, ensuring ethical data collection and management. | Design and embed safeguards into AI systems to mitigate psychological, societal, and security risks across diverse contexts |
| 28. Safe and Responsible Use of AI | Demonstrate responsible behaviours when using AI tools, recognising potential misuse. | Apply ethical frameworks to evaluate AI’s impact on individuals and society, considering fairness and justice. | Use a critical mindset to question assumptions and limitations of AI tools | Demonstrate a safety-first, equitable and accessible approach in AI development, taking responsibility for system impacts and long-term consequences |
| 29. AI and Cultural Diversity | Discuss the impact of AI on cultural diversity and identify risks of cultural bias. | Recognise how AI can impact minority cultures, including Māori and Pacific peoples, and apply principles of Indigenous data sovereignty. | Advocate for inclusive AI applications by addressing diverse user needs and avoiding discriminatory impacts. | Lead the design and implementation of inclusive AI systems that respect cultural diversity and social justice. Design safeguards that not only protect privacy and security but also respect cultural protocols, indigenous concepts of collective rights |