| Foundational Understanding |
What is AI and how does it work conceptually? |
- Generative, non-generative, agentic AI
- Data training and limitations
- AI as tool, not intelligence
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- Explain how LLMs generate responses.
- Differentiate between AI capabilities and human reasoning.
- Explain how training data shapes AI outputs.
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| Functional Skills |
How can I use AI tools effectively? |
- Prompt design and iteration
- Selecting appropriate tools for specific learning tasks
- Adjusting instructions and tone
- Recognizing tool limitations
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- Construct structured prompts for specific academic tasks.
- Demonstrate iterative refinement of AI outputs.
- Select and justify tool choice for a task.
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| Critical Evaluation |
How can I assess AI outputs for quality and reliability? |
- Fact-checking claims
- Comparing to trusted sources
- Spotting hallucinations and bias
- Evaluating rhetorical stance
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- Identify inaccurate or vague claims in AI outputs.
- Cross-verify information with external credible sources.
- Evaluate rhetorical stance and bias in AI outputs.
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| Rhetorical & Ethical Awareness |
When is AI use appropriate and why? |
- Disciplinary norms and boundaries
- Ethical use and attribution
- Maintaining authorship and intellectual agency
- Knowing when not to use AI
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- Justify your choices about AI use in a specific academic task.
- Identify ethical issues in various use cases.
- Explain how rhetorical stance shapes responsible use.
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| Transparency & Metacognition |
How can I reflect on and disclose my AI use? |
- Process documentation
- AI use disclosure statements
- Reflection on tool influence
- Identifying learning vs outsourcing
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- Write a clear AI transparency statement describing your human value and AI contribution.
- Explain how AI shaped your thinking and how you shaped the outcome.
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