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Distinguish between the different types of AI and their historical development
Assessment Strategies
Written Product
Criteria
Define artificial intelligence and distinguish it from traditional rule-based programming
Describe key milestones in the history of AI, from early expert systems to modern generative A
Distinguish between narrow AI, general AI, and generative AI
Explain how machine learning differs from traditional programming approaches
Identify real-world examples of different AI types and their applications
Explain what large language models (LLMs) are and how they generate responses
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Compare major AI platforms
Assessment Strategies
Written Product - Comparative Analysis
Criteria
Identify major AI platforms and tools, including ChatGPT, Claude, Gemini, Copilot, and others
Compare AI platforms based on capabilities, strengths, limitations, and appropriate use cases
Select an effective AI tool for a given task
Justify the selection of an AI tool
Evaluate the output quality of different platforms given the same prompt
Use at least two different AI platforms
Describe the access models and usage limits of major AI platforms, including free, paid, and institutional tiers
Explain how limit types (message caps, context windows, and feature gates) affect platform availability
Select platforms strategically based on task requirements and current usage constraints
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Write prompts that effectively communicate intent to AI systems and produce high-quality outputs
Assessment Strategies
Skill Demonstration - Prompt Lab
Criteria
Identify the components of an effective prompt, including context, instruction, and desired output format
Prompts produce focused, relevant, and high-quality AI response
Prompts are clear and specific
Identify common prompting errors and explain how to correct them
Correlate prompt specificity to output quality through comparison examples
Use iterative refinement to improve a prompt based on AI output
Apply prompt structure strategies such as specifying role, format, tone, and constraints
Explain how prompt specificity reduces unnecessary iteration and contributes to efficient use of platform message limits and resources
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Apply advanced prompting techniques including chain-of-thought reasoning, few-shot examples, role-playing, and system prompts to improve AI responses
Assessment Strategies
Project
Criteria
Apply chain-of-thought prompting to guide AI through multi-step reasoning tasks
Use few-shot examples within a prompt to shape AI output style and format
Construct role-playing prompts that assign a persona or area of expertise to an AI system
Write and evaluate system prompts that set context, tone, and behavioral constraints
Compare the effectiveness of basic versus advanced prompting techniques for a given task
Combine multiple advanced techniques in a single prompt to optimize AI output
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Evaluate AI-generated output critically, identifying strengths, weaknesses, biases, and opportunities for refinement
Assessment Strategies
Written Product - Analysis
Criteria
Assess AI-generated content for factual accuracy and identify potential hallucinations
Identify bias in AI output, including cultural, demographic, and perspective-based bias
Evaluate the relevance, completeness, and quality of AI responses against the original prompt
Apply a structured framework to critique AI output and document findings
Propose specific prompt revisions to address weaknesses in AI-generated content
Explain why AI systems may produce inaccurate, incomplete, or biased results
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Design and implement personal AI workflows that integrate multiple tools and platforms into coherent processes
Assessment Strategies
Project
Criteria
AI-assisted workflow addresses a real-world task or problem that can be improved
Workflow is multi-step and integrates effective AI tools at each stage
Workflow is implemented by executing the process using selected AI platforms
Workflow is documented, including inputs, AI tools used, prompts, outputs, and refinements
Evaluate the effectiveness of the workflow
Identify opportunities for workflow improvement
Demonstrate measurable gains in productivity or quality achieved through the AI workflow
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Analyze the limitations, ethical considerations, and responsible use of AI systems
Assessment Strategies
Written Reflection
Criteria
Identify key limitations of AI systems, including hallucination, bias, and knowledge cutoffs
Explain intellectual property and copyright considerations when using AI-generated content
Describe privacy risks associated with sharing sensitive information with AI tools
Apply institutional and professional guidelines for the responsible use of AI
Evaluate the ethical implications of AI use in academic, workplace, and personal contexts
Articulate a personal framework for responsible AI use and decision-making