100 : AI Foundations

AI Foundations 100 Curriculum Structure & Learner Modules

The curriculum is competency-based and organized into six modules that can be completed in 2–5 weeks. Each module includes clear, measurable learning outcomes, low-or no-cost learning resources, and embedded comprehension checks (quizzes, reflections, and applied tasks). Learners must demonstrate competency through performance-based assessments, culminating in a capstone project where they design and document an AI-supported workflow relevant to their current or desired occupation.

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Module 0 – Orientation & AI in Ohio's Workforce Contact Hours: 2–3 hours

Learning Objectives:

• Explain course expectations, schedule, technology requirements, and support resources
• Describe how AI is changing work in Ohio's key sectors (healthcare, manufacturing, logistics, education, services)
• Identify personal goals for AI skill development and career application

Content Topics:

• Course overview, outcomes, and assessment structure
• Introduction to Ohio's AI workforce initiatives (AI Ready Ohio, Ohio State AI Fluency Initiative)
• Discussion: "Where is AI already showing up in my job or desired career?"
• Technology setup and access verification (learning management system, AI tools, communication platforms)

Free Learning Resources:

• Article: JobsOhio and Enterprise Technology Association "AI Ready Ohio" pilot overview
• Reading: Ohio State AI Fluency Initiative summary
• Optional: Video tour of course platform and AI tool access

Learning Activities:

• Pre-course technology and AI familiarity survey
• Reflective prompt: "List three tasks in your job search, current job, or daily life that might be improved with AI"
• Facilitated discussion (in-person or virtual breakout groups): Sharing AI experiences and goals

Assessment/Comprehension Check:

• 5-question multiple-choice quiz covering course expectations and Ohio AI workforce context
• Completion of introductory survey and reflection (graded for completion).

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Module 1 – Introduction to AI & Key Concepts Contact Hours: 4–6 hours

Learning Objectives:

• Define artificial intelligence in accessible, non-technical terms
• Distinguish between AI, machine learning, and generative AI
• Identify common AI applications in everyday tools and workplace software
• Explain in simple language what AI can and cannot do

Content Topics:

• What is AI? Everyday examples (search engines, recommendations, spam filters, voice assistants, chatbots)
• Key terminology: artificial intelligence, machine learning, data, model, training, inference, generative AI, large language model
• Traditional rule-based systems learning-based AI systems
• Myths realities: capabilities and limitations of current AI technologies
• Real-world AI applications in Ohio industries

Free Learning Resources:

• Google AI "Learn AI Skills" – Introduction to Generative AI (video lessons)
• Elements of AI (elementsofai.com) – Introduction to AI module (selected readings)
• HP LIFE "AI for Beginners" course – Sections on AI basics and terminology
• Curated YouTube explainer videos on "What is AI?" and "AI in everyday life"

Learning Activities:

In-Person/Live:

• Guided video walk-through with pause-and-discuss segments to clarify key terms
• "AI Spotting" brainstorm activity: Learners identify AI in phones, apps, workplaces; facilitator groups examples by AI type
• Hands-on demo: Instructor demonstrates a general-purpose AI assistant (e.g., free chatbot) summarizing text, generating checklists, rewriting content

Online/Asynchronous:

• Watch assigned videos and read Elements of AI selected sections
• Discussion forum post: "Share one AI use that surprised you and explain why"
• Self-guided exploration: Use a free AI tool to complete one simple task (summarize an article, generate a list)

Assessment/Comprehension Check:

• 10-question multiple-choice/true-false quiz covering AI definitions, examples, and capabilities
• Terminology matching worksheet (AI term → plain-language definition)
• Graded discussion post demonstrating understanding of AI applications

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Module 2 – Machine Learning & Generative AI Basics Contact Hours: 4–6 hours

Learning Objectives:

• Explain in simple language how machine learning models are trained using data
• Describe what makes generative AI different from traditional rule-based systems
• Recognize examples of machine learning and generative AI in workplace and consumer tools
• Identify limitations of AI models, including bias, errors, and hallucinations

Content Topics:

• "Learning from data" – how AI systems identify patterns and make predictions
• Basic concepts: supervised learning, unsupervised learning (explained with simple analogies)
• Examples of machine learning: recommendation systems, fraud detection, image recognition, predictive text
• Generative AI: text generation, image generation, code generation, and creative applications
• Understanding limitations: hallucinations (AI generating false information), training data issues, bias in AI outputs, domain mismatch

Free Learning Resources:

• HP LIFE "AI for Beginners" – Machine learning and use cases sections
• Elements of AI – Units on "How AI systems learn" and "What AI can and cannot do"
• Google AI "Introduction to Generative AI" short course videos
• Selected YouTube videos explaining machine learning with real-world examples

Learning Activities:

In-Person/Live:

• Card-sorting activity: Learners categorize workplace scenarios into "rules-based automation," "traditional machine learning," or "generative AI"
• Group exercise: For a given job role (customer service, logistics, healthcare support), identify which tasks fit each AI type

• Instructor-led demo: Show examples of AI successes and failures (accurate predictions vs. hallucinations or bias)

Online/Asynchronous:

• Complete selected Elements of AI readings on learning and limitations
• Watch one Google generative AI video and one machine learning explainer
• Reflection discussion post: "Describe a time when an AI or automated system got something wrong. What might have caused that error?"

Hands-On  Micro-Tasks:

• Text generation practice: Use a free AI chatbot to generate three email subject lines for a job application; evaluate quality
• Prompt experimentation: Ask AI to explain a simple topic (e.g., photosynthesis) at three levels (child, teenager, professional) and observe differences in outputs
• Optional image generation: Use a free tool to create a simple work-related image and note any inaccuracies

Assessment/Comprehension Check:

• 10–12 question scenario-based quiz: Identify which AI type is being used and describe one limitation or risk
• Short written assignment (1 page): "Explain how machine learning works using an analogy" (graded for clarity and accuracy)
• Graded reflection post demonstrating understanding of AI limitations

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Module 3 – Using Everyday AI Tools for Work Contact Hours: 4–8 hours

Learning Objectives:

• Use at least one AI tool effectively for text creation, editing, or summarization
• Use AI to support basic data tasks (organizing, categorizing, brainstorming, drafting procedures)
• Apply effective prompt strategies to improve the quality and accuracy of AI outputs
• Demonstrate human oversight by editing and verifying AI-generated content

Content Topics:

• Overview of AI-enhanced tools commonly available: office suites (Microsoft 365, Google Workspace), email assistants, browser extensions, job search platforms
• Using AI for workplace tasks:
  • Document and meeting summaries
  • Brainstorming and idea generation
  • Drafting and revising professional writing (emails, cover letters, reports, Standard Operating Procedures)
  • Creating checklists, templates, and job aids
• Introduction to effective prompting: providing context, specifying role/audience, giving examples, setting constraints
• Best practices: saving time while maintaining accuracy, clarity, and personal voice

Free Learning Resources:

• Google AI "Learn AI Skills" – Lessons on using generative AI in everyday tools and prompt strategies
• HP LIFE "AI for Beginners" – Practical generative AI use sections
• Sample prompt libraries and templates (provided by instructor or curated from free online resources)
• Optional YouTube tutorials on "How to write effective AI prompts"

Learning Activities:

In-Person/Live:

• Instructor-led demonstration:
  • Use an AI tool to summarize a one-page policy document or job posting
  • Ask AI to rewrite a paragraph for different audiences (employer, customer, colleague)
  • Show before-and-after examples of prompt refinement

• Guided hands-on lab: Learners bring a real or sample document (resume, email, work announcement) and:
  • Use AI to generate suggestions or a draft
  • Edit the AI output to ensure accuracy, tone, and personal voice
  • Compare original and revised versions

Online/Asynchronous:

• Self-paced mini-assignment:

1. Choose a work-related scenario (job search email, customer response, shift instructions, meeting summary)
2. Use an AI tool to generate a draft
3. Edit and improve the draft
4. Submit both the AI-generated version and the final human-edited version with a brief explanation of changes made

• Discussion forum: Share one prompt that worked well and one that did not; explain why

Hands-On  Micro-Tasks:

• Create a simple workplace checklist or standard operating procedure with AI assistance
• Use AI to generate interview questions for a specific job title, then answer one question in your own words
• Draft a professional email requesting information or responding to a customer inquiry

Assessment/Comprehension Check:

• Practical skills check: Instructor reviews submitted work artifact (email, summary, checklist, SOP) for evidence of AI-assisted drafting plus thoughtful human editing
• Short quiz (10 questions) on prompt strategies and best practices for AI-assisted writing
• Self-assessment rubric: Learners rate their confidence in using AI for common workplace tasks

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Module 4 – Ethics, Policy, & Responsible AI Use Contact Hours: 4–6 hours

Learning Objectives:

• Identify key ethical concerns in AI use: bias, fairness, privacy, transparency, and accountability
• Explain why human judgment and oversight are essential in AI-supported work
• Describe basic principles from Ohio's AI model policy and apply them to workplace contexts
• Recognize scenarios where AI use may be inappropriate or require additional safeguards

Content Topics:

• Introduction to AI ethics: why ethics matter in hiring, lending, education, healthcare, and public services
• Major ethical issues:
  • Data privacy and confidentiality (protecting personal and proprietary information)
  • Bias and discrimination (how AI can perpetuate or amplify unfairness)
  • Transparency and explainability (understanding how AI decisions are made)
  • Misuse and over-reliance (risks of trusting AI without verification)
• Organizational and policy responses:
  • Overview of ethical AI frameworks and guidelines
  • Summary of Ohio AI Model Policy principles for responsible use in educational and workforce settings
• Personal responsibility: thinking critically about when and how to use AI, verifying outputs, protecting sensitive information

Free Learning Resources:

• Oxford Home Study Centre "Ethics in AI" free course (selected readings and modules)
• University of Helsinki "Ethics of AI" free online course (selected units)
• Ohio Department of Education "AI Model Policy for Ohio Districts and Schools" (summary and key points adapted for adult workforce learners)
• Curated articles and case studies on AI bias, privacy concerns, and transparency issues

Learning Activities:

In-Person/Live:

• Case study small-group analysis:
  • Scenario: "An employer uses AI to screen What could go wrong? How should the company respond?"
  • Groups identify potential risks (bias, privacy, transparency) and propose safeguards
• Role-play exercise: One learner plays an AI system that made an error; another plays a supervisor deciding how to respond and prevent future issues
• Facilitated discussion: "When should you NOT use AI in your job or training?"

Online/Asynchronous:

• Read a short ethics lesson from Oxford or Helsinki course and answer guided reflection questions in discussion board
• Watch a short video on AI bias or privacy and post a summary with personal insights
• Scenario-based reflection: "Describe one way you will use AI responsibly in your future work and explain why that approach is important"

Assessment/Comprehension Check:

• Scenario-based quiz (10–12 questions, multiple choice and short answer) focusing on:
  • Identifying potential ethical harms in realistic workplace situations
  • Choosing appropriate human oversight and verification actions
  • Applying principles from Ohio AI policy

• 1-paragraph personal commitment statement on responsible AI use (graded for completion, thoughtfulness, and application of course concepts)

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Module 5 – Capstone: AI-Enhanced Workflow Project Contact Hours: 4–8 hours

Learning Objectives:

• Identify a realistic work or life task that can be improved using AI tools
• Plan and execute a simple AI-supported workflow, documenting steps, prompts, and decisions
• Demonstrate effective use of AI combined with human oversight and editing
• Reflect on benefits, limitations, and ethical considerations for the chosen use case
• Articulate the value of AI skills to employers in resumes and interviews

Project Description:

Learners design and complete a small, applied project using AI to improve a task in one of the following areas:

• Job search and career readiness: Resume optimization, cover letter drafting, interview preparation, LinkedIn profile enhancement
• Customer or client communication: Email templates, FAQ development, customer service scripts, response drafts
• Administrative efficiency: Checklists, schedules, meeting summaries, standard operating procedures, training materials
• Learning and training support: Study guides, lesson plan drafts (for educators or trainers), knowledge base articles

Project Components:

1. Proposal (written or video, 1–2 paragraphs):
   • Describe the task and current challenges or inefficiencies
   • Explain how AI could assist with specific steps (drafting, organizing, brainstorming, summarizing)

• Identify the AI tool(s) you plan to use

2.  Implementation:

• Use at least one AI tool to support the task
• Document:
  • At least two example prompts you used
  • AI-generated outputs (screenshots, saved text, or exported files)
  • Your edits and revisions to the AI outputs
• Save or compile artifacts showing before-and-after or process steps

3.  Reflection (1–2 pages written or short slide deck/video):

• What did the AI tool do well?
• What did you have to correct, add, or change?
• What ethical, accuracy, or appropriateness issues did you notice or consider?
• How would you use this AI-supported workflow in your job or career pathway?
• How will you explain this new skill to employers?

Delivery Options:

In-Person/Live:

• Project work sessions with instructor and peer support available
• Optional short presentations (3–5 minutes) or gallery walk where learners share key takeaways and artifacts
• Peer feedback sessions using structured rubrics

Online/Asynchronous:

• Projects submitted through learning management system as documents, slide decks, or screen-share videos
• Peer review via discussion boards or LMS peer-review tools (optional)
• Instructor feedback provided within 5 business days

Capstone Rubric (IMAP-Aligned):

Criterion	Points	Description
Problem Definition and Relevance	25%	Task is clearly described, relevant to in-demand work or career goals, and appropriate for AI assistance
Appropriate and Effective Use of AI Tools	25%	AI tool(s) used correctly; prompts are clear and effective; outputs demonstrate understanding of AI capabilities
Evidence of Human Oversight and Revision	25%	Learner edited and improved AI outputs; demonstrated critical thinking and quality control
Reflection on Ethics, Limitations, and Future Use	25%	Thoughtful analysis of benefits and risks; clear plan for applying AI skills in workplace; awareness of responsible use principles

Assessment/Comprehension Check:

• Capstone project submission evaluated using rubric above (must achieve 70% or higher to earn microcredential)
• Self-assessment: Learners rate their confidence in using AI tools independently and responsibly after completing the project

Summative Assessment and Credential Award Final Knowledge Check:

• 20–30 question comprehensive exam covering:
  • Key AI concepts (definitions, types, capabilities, limitations)
  • Everyday AI applications and appropriate use cases
  • Prompt strategies and best practices for AI-assisted work
  • Ethical and policy considerations for responsible AI use
• Passing score: 70% or higher

Credential Requirements:

To earn the AI Foundations Microcredential, learners must:

• Complete all six modules and participate in required activities
• Pass all module comprehension checks (quizzes, reflections, assignments) with 70% or higher
• Successfully complete the capstone AI-enhanced workflow project with a score of 70% or higher
• Pass the final knowledge check with a score of 70% or higher
• Complete post-course survey and employment/training outcome follow-up (required for IMAP reporting)

Microcredential Award:

Upon successful completion, participants will receive the AI Foundations Microcredential issued by PD4ME. This microcredential verifies that the earner can:

• Describe core AI concepts in accessible language
• Use common AI tools to support workplace communication and productivity
• Apply basic ethical and policy guidelines to responsible AI use in professional settings

The microcredential is designed as a stackable credential that can stand alone for immediate employment value or articulate into additional digital skills, information

technology, or industry-specific training programs offered by [Institution Name] and partner providers.

Career Support:

• Learners receive guidance on adding AI skills to resumes and LinkedIn profiles
• Sample language provided for discussing AI competencies in job interviews
• Optional job placement support and employer connections through [Institution's Career Services or Workforce Development Office]

Learner Support and Equity

To promote equitable outcomes, the program incorporates proactive learner supports, including:

• Technology orientation and access: Assistance with learning management system setup, AI tool access verification, and optional digital skills refreshers
• Tutoring and office hours: Virtual or in-person support available [specify days/times or "by appointment"]
• Career coaching: Help translating AI skills into resume language, interview talking points, and job search strategies
• Flexible scheduling: Evening, weekend, and fully asynchronous options to accommodate work schedules, transportation challenges, and caregiving responsibilities
• Plain language and multiple formats: Course materials use accessible language and offer video, text, and hands-on practice options to support learners with varying levels of digital literacy

• Ongoing communication: Regular check-ins via email, text, or LMS messages to support persistence and completion

IMAP Reporting and Outcomes

The provider will collect and report IMAP-required data on enrollment, completion, demographics, and employment-related outcomes. Success will be measured by:

• Microcredential completion rate (target: 70% or higher)
• Learner self-reported confidence in using AI tools at work (pre/post surveys)
• Employment outcomes: Job placement, promotion, wage increase, or entry into additional training within 6 months.
• Employer or partner feedback on the relevance and value of the skills gained : collected through learner/employer surveys or focus groups.

Aggregate results will be used to refine curriculum, strengthen employer engagement, and ensure continued alignment with Ohio's in-demand occupations and AI workforce initiatives.

Instructor Qualifications

Instructors for AI Foundations must have:

• Experience in workforce development, adult education, information technology, or related field
• Demonstrated proficiency in using AI tools (generative AI, machine learning applications, productivity software with AI features)
• Knowledge of ethical AI principles and Ohio's AI policy framework
• Strong facilitation skills for adult learners with diverse backgrounds and digital literacy levels

Preferred qualifications:

• Credential or certification in AI, data science, instructional design, or educational technology
• Experience delivering competency-based or microcredential programs
• Connections to Ohio employers or workforce development networks