Here’s a framework that leverages young adults’ existing educational abilities to learn AI and connect these skills to career competencies, such as those outlined by the National Association of Colleges and Employers (NACE):

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Framework for AI Learning Aligned with NACE Competencies

1. Core Components of Existing Educational Abilities

Young adults often have these foundational abilities from their education:

1. Basic Digital Literacy: Familiarity with technology and digital tools.
2. Problem-Solving Skills: Ability to analyze and address challenges.
3. Critical Thinking: Evaluating information and drawing reasoned conclusions.
4. Communication Skills: Expressing ideas clearly in writing and speech.
5. Collaboration: Working effectively in teams.
6. Creativity: Generating novel ideas and approaches.
7. Self-Management: Prioritizing tasks and managing time effectively.
8. Numeracy and Data Interpretation: Understanding and using data in decision-making.

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2. Leveraging Abilities to Build AI Skills

These foundational abilities can be enhanced to learn AI in a way that supports career development:

1. Basic Digital Literacy → Computational Thinking
   • Transition from using digital tools to understanding algorithms and how machines process information.
   • Learning tools: Block-based coding platforms (e.g., Scratch) or no-code AI tools (e.g., Runway ML).
   • NACE Competencies: Critical Thinking, Technology
2. Problem-Solving Skills → Algorithm Design
   • Learn to design simple algorithms to automate repetitive tasks or solve problems.
   • Use cases: Automating tasks with chatbots (e.g., ChatGPT) or designing workflows.
   • NACE Competencies: Critical Thinking, Problem-Solving
3. Critical Thinking → Ethical AI Use
   • Explore the implications of AI in society, focusing on bias, transparency, and privacy.
   • Learning tools: Case studies and discussions on real-world AI ethics issues.
   • NACE Competencies: Equity & Inclusion, Critical Thinking
4. Communication Skills → AI-Powered Storytelling
   • Use AI tools for generating content, summarizing information, or creating multimedia presentations.
   • Learning tools: AI writing assistants (e.g., Jasper, Canva AI).
   • NACE Competencies: Communication
5. Collaboration → Team-Based AI Projects
   • Participate in group projects using collaborative AI tools to solve real-world problems.
   • Example: Using Miro or Notion integrated with AI for brainstorming or project management.
   • NACE Competencies: Teamwork, Collaboration
6. Creativity → AI Augmented Creativity
   • Experiment with generative AI tools (e.g., DALL-E, MidJourney) to create visual, audio, or textual content.
   • NACE Competencies: Creativity, Innovation
7. Self-Management → AI-Driven Productivity
   • Learn to use AI for personal productivity, like scheduling, prioritizing tasks, or managing workflows.
   • Tools: AI task managers (e.g., Notion AI, Asana AI).
   • NACE Competencies: Self-Management
8. Numeracy and Data Interpretation → Data Analysis with AI
   • Use AI tools (e.g., Tableau, Google Sheets with AI plugins) to interpret data and generate insights.
   • NACE Competencies: Analytical Skills

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3. Career Skill Alignment

AI learning activities can be directly mapped to NACE Career Readiness Competencies:

NACE Competency	AI Learning Activity
Critical Thinking & Problem Solving	Develop algorithms for real-world problems. Analyze AI-generated outputs for accuracy.
Communication	Use AI tools for presentations, writing, and storytelling.
Equity & Inclusion	Study ethical considerations and ensure inclusive AI design.
Teamwork & Collaboration	Work on AI-powered group projects.
Professionalism	Manage time effectively using AI tools for scheduling and prioritization.
Technology	Learn and apply AI tools for data analysis, content creation, and automation.
Leadership	Lead AI project teams, making decisions on tools, processes, and outcomes.
Career & Self-Development	Explore AI applications in industries of interest and build portfolios showcasing skills.

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4. Pathways to Advance AI Learning

1. Scaffolded Learning:
   • Start with simple, interactive tools and gradually introduce more complexity (e.g., from Scratch to Python).
2. Contextual Learning:
   • Teach AI applications in contexts relevant to students’ interests (e.g., AI in art, business, or healthcare).
3. Project-Based Learning:
   • Design hands-on projects that simulate real-world challenges (e.g., building an AI-powered resume screener).
4. Integration with Career Planning:
   • Provide examples of how AI is used in specific career fields, helping students see tangible connections.
5. Capstone Experience:
   • Create a final portfolio demonstrating proficiency in AI tools and their application to career scenarios.

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By aligning AI education with existing abilities and career readiness competencies, this framework helps young adults effectively learn AI while preparing for the workforce.