Understanding Data
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Foundational Data Literacy
Develop understanding of foundational data literacy concepts, including principles of data sovereignty and critical evaluation of sources.
Examples:
- Understand basic criteria for finding and assessing data sources.
- Understand core principles and institutional policies related to misinformation and privacy.
- Consider the importance of data sources in evaluating AI tools and outputs.
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Critical Data Literacy
Critically interrogate data foundations of AI tools and systems, considering sources of potential bias and principles related to Indigenous knowledge and data governance.
Examples:
- Apply evaluation frameworks to assess the appropriateness of AI tools and outputs based on their data foundations.
- Apply core concepts of critical information literacy to understand social and cultural implications of AI data practices.
- Understand institutional and sector-wide commitments related to Indigenous knowledge and data governance.
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Applied Data Literacy
Incorporate rigorous data literacy approaches into AI use and evaluation of AI outputs, and apply principles of OCAP* and CARE** to data governance and management in the context of AI.
Examples:
- Apply principles of OCAP and CARE across the spectrum of AI use, from individual practice to team and institutional policies and frameworks.
- Apply critical information literacy skills to evaluate and mitigate the validity of AI outputs.
- Observe and/or establish appropriate data governance protocols.
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AI Tools and Systems
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Foundational AI Awareness
Develop a basic understanding of AI concepts, how AI systems function, and the role of data in AI decision-making.
Examples:
- Understand the importance of verifying AI-driven insights with human judgement.
- Understand basic evaluation criteria for AI-generated content, such as accuracy, consistency, and source reliability.
- Identify a number of inconsistencies or biases in AI-generated content.
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AI in Action
Select AI tools for real-world tasks, understand how AI models work, and assess the role of data in AI performance.
Examples:
- Apply evaluation frameworks to assess the validity of AI-generated insights.
- Identify and articulate biases or inconsistencies in AI-generated output.
- Compare AI-generated information against multiple independent sources for verification.
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AI Optimization
Critically engage with AI systems, assess their technical capabilities, and strategically integrate AI into decision-making.
Examples:
- Apply logical reasoning to understand how AI generates responses, analyze the strengths and weaknesses of different AI models and their output, and effectively build upon them.
- Effectively leverage AI capability to support critical thinking skills.
- Recognize and manage the nuanced impacts of AI in complex, high-stakes situations.
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Critical Thinking and Judgment
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Question AI Use and Output
Identify key criteria for deciding whether to use AI tools and for evaluating AI output, and understand that AI-generated content may contain biases or errors
Examples:
- Understand the importance of verifying AI-driven insights with human judgement.
- Understand basic evaluation criteria for AI-generated content, such as accuracy, consistency, and source reliability.
- Identify a number of inconsistencies or biases in AI-generated content.
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Evaluate AI Use and Output
Critically evaluate AI use cases and assess alternatives, and critically evaluate AI AI-generated content using established evaluation criteria and identify biases or inconsistencies.
Examples:
- Apply evaluation frameworks to assess the validity of AI-generated insights.
- Identify and articulate biases or inconsistencies in AI-generated output.
- Compare AI-generated information against multiple independent sources for verification.
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Challenge AI Use and Output
Demonstrate expertise in evaluating AI-generated output with rigorous methodologies, interrogating AI's reasoning processes, and assessing AI's impact on human cognition.
Examples:
- Apply logical reasoning to understand how AI generates responses, analyze the strengths and weaknesses of different AI models and their output, and effectively build upon them.
- Effectively leverage AI capability to enhance critical thinking skills.
- Recognize and manage the nuanced impacts of AI in complex, high-stakes situations.
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Ethical and Responsible Use
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Understand Principles
Understand fundamental principles of AI ethics and can recognize potential risks, such as bias, misinformation, discrimination, and impacts on Indigenous communities.
Examples:
- Define key AI ethics principles (e.g. fairness, transparency, accountability, privacy).
- Recognize how AI systems can perpetuate bias and inequality.
- Identify ethical concerns in AI-driven decision-making (e.g. hiring, surveillance, law enforcement)
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Apply Responsible Practices
Apply ethical principles and frameworks to make informed decisions about the use and application of AI and to mitigate risks or harms associated with AI use.
Examples:
- Assess AI systems for compliance with ethical standards and legal frameworks.
- Identify and mitigate risks related to bias, discrimination, and data privacy in AI applications.
- Implement strategies to ensure fairness and accountability in AI decision-making
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Shape Responsible Practices
Demonstrate expertise in evaluating, shaping, and advocating for ethical AI policies, governance frameworks, and institutional practices consistent with the university’s values and commitments.
Examples:
- Critically evaluate ethical implications of AI adoption at an institutional or societal level.
- Contribute to the development of AI governance frameworks and ethical AI policies.
- Provide guidance on ethical AI adoption in professional, academic, or policy environments.
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Human-Centricity, Emotional Intelligence, and Creativity
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Awareness of Human-AI Interaction
Have a foundational understanding of how AI affects human decision-making, communication, and emotional intelligence.
Examples:
- Recognize how AI influences human behaviour, decision-making, and interactions.
- Identify situations where AI may lack human sensitivity (e.g. AI-generated feedback, automated decision-making).
- Understand the importance of empathy and adaptability in AI-augmented environments.
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AI as Collaborative Tool
Able to integrate AI skills into human-centred environments to promote responsible, ethical, and inclusive AI use.
Examples:
- Apply effective communication strategies and human-in-the-loop strategies when using AI tools in professional and educational settings.
- Identify opportunities to enhance human-centred skills and foster creative thinking with AI, and propose strategies for continued development.
- Assess AI tools to ensure inclusivity for different user groups.
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Develop Human-Centred AI Practices
Advocate for human-centred AI approaches, ensuring AI remains a tool that complements rather than replaces human skills.
Examples:
- Develop AI-driven workplace or education policies that safeguard human agency in decision-making.
- Establish guidelines for using AI in professional or educational environments that ensure AI complements, rather than replaces, human interaction and creativity.
- Conduct empirical studies or pilots testing the impact of AI in human-centred roles.
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Domain Expertise
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Applied AI Awareness
Develop a basic understanding of how AI is used in their specific field and can identify relevant AI tools and applications.
Examples:
- Identify key AI applications relevant to a specific domain (e.g. AI in medicine, law, education, finance).
- Recognize how AI is transforming professional roles and industry standards.
- Understand the basic limitations of AI when applied in a particular field.
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AI Application in Professional Contexts
Where relevant, effectively use AI tools to support tasks, optimize workflows, and improve decision-making within their discipline.
Examples:
- Select and apply AI tools that enhance efficiency and accuracy in a professional or academic setting.
- Assess the strengths and weaknesses of AI applications within specific processes or parts of the value chain.
- Integrate AI insights into professional decision-making while understanding AI’s role as a complement to human expertise.
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Strategic AI Leadership
Develop advanced expertise in AI applications within their discipline, ensuring AI is effectively integrated into strategic decision-making where relevant.
Examples:
- Evaluate and refine AI adoption strategies within the field, considering regulatory, ethical, and operational constraints.
- Lead the implementation of AI-driven innovations in a professional or academic context.
- Develop training materials or guidelines to enhance AI literacy among peers and colleagues in the field.
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