AI Solutions for Practical Challenges

Learning Outcomes & AI Competencies

Upon completing this certification, learners will be able to:

Understand Generative AI Fundamentals

Explain core concepts of AI and machine learning, especially how generative models create new content (text, images, audio) using neural networks and large datasets.

Apply Prompt Engineering Techniques

Use effective prompt strategies to work with large language models (LLMs) like OpenAI’s ChatGPT, guiding AI systems to produce accurate and relevant outputs. This includes employing prompt patterns (e.g. zero-shot, few-shot, chain-of-thought) to leverage advanced model capabilities.

Develop AI-Driven Solutions

Design and refine complex prompt-based applications for real-world scenarios in business, education, or personal projects. Learners will integrate generative AI tools into problem-solving tasks, demonstrating creativity and productivity gains through AI augmentation.

Ethical and Responsible AI Use

Evaluate the ethical implications of AI solutions, including issues of bias, fairness, data privacy, and academic integrity. Learners will understand responsible AI design and usage, recognizing limitations of AI systems and ensuring compliance with ethical guidelines and societal expectations.

AI Literacy and Collaboration

Interpret AI outputs critically and collaborate effectively with AI systems as co-creators. This entails understanding when and how to trust AI-generated content, verifying information, and using AI to enhance (not replace) human decision-making.

Career and Industry Awareness

Identify high-growth career pathways in AI and data science (e.g. prompt engineer, machine learning engineer, NLP specialist, AI ethicist) and the skills or education needed for each. Learners will connect their skills to workforce opportunities, in line with Florida’s goal of preparing students for an AI-driven economy.

Tools, Platforms & Software Mastery

This certification emphasizes hands-on proficiency with a range of AI tools and development platforms. Learners will gain experience with:

Large Language Model Platforms: OpenAI’s ChatGPT (GPT-4), Anthropic Claude, Google Bard.
Generative AI for Images/Media: DALL·E 3, Stable Diffusion, emerging audio/video platforms.
AI Integration & Scripting: Python libraries, web APIs, GitHub, GitHub Copilot.
Collaborative AI Tools: Microsoft 365 Copilot, Google Workspace AI tools.
Data and Cloud Platforms: Hugging Face, Microsoft Azure AI, Google Cloud Vertex AI.

Rationale: Mastering these tools aligns with industry expectations. Including multiple vendors ensures the curriculum remains vendor-neutral and current. GitHub and developer tools build habits of versioning and collaboration.

Course Structure & Instructional Sequence

The curriculum is organized into modules that scaffold from foundational knowledge to advanced application. A recommended sequence is:

Introduction to AI and Generative Models

Covers AI basics, terminology, generative AI overview, model training, analysis vs. generation. Embeds ethical AI considerations (bias, privacy).

Prompt Engineering Fundamentals

Introduces effective prompt crafting, context setting, few-shot examples, prompt patterns (role prompt, step-by-step reasoning), and best practices.

Generative AI Tools in Practice

Hands-on exploration of ChatGPT, Google Bard, image generators. Guided labs for text summarization, coding assistance, image creation, comparing platforms.

Domain Applications of Generative AI

Applying AI in business (emails, reports), education (tutoring), creative industries (content), software development (code generation/review). Scenario-based problems.

Project Design and AI Integration

Incorporating AI into workflows/products. Basic scripting for APIs, automation tools (Zapier), handling errors/biases, user feedback, ethical transparency.

Capstone Project and Portfolio

Design, implement, and showcase a generative AI solution to a real-world problem. Exam preparation, reflection, portfolio compilation (prompts, code, report).

Instructional Strategies: Project-based learning, iterative practice. Early modules: structured labs. Later modules: open-ended projects. Ethical/societal impacts discussed throughout. "Hands-on, then minds-on" approach.

Possible Implementation: High schools: 1 full-year course or 2 semesters. Postsecondary/adult: shorter intensive courses/workshops. Adaptable based on prior coding experience.

Performance Assessments, Micro‑Credentials & Capstones

Learners demonstrate competencies through performance-based assessments and culminating projects. Key components include:

Hands-On Prompting Challenges

Iteratively refine poorly performing AI prompts to meet criteria (accuracy, tone, completeness). Submissions include final prompt, AI response, and analysis.

Project-Based Micro-Credentials

“Prompt Engineering Basics” Badge
“AI Ethics & Responsible Use” Badge
“AI Integration Apprentice” Badge

Capstone Project

Synthesizes all skill areas. Example: develop a conversational AI assistant for a community or business need (e.g., SmartTown.ai civic chatbot). Present project, demo, and reflection.

Industry Certification Exam: Program can culminate in an industry exam if available, or capstone serves as certification assessment.

Feedback and Iteration: Students maintain a “Prompt Portfolio” for regular feedback, reinforcing critical thinking and ethical awareness.

Micro-Credentials Integration: Offered in stages for flexible delivery, stacking toward full certification.

Crosswalk to Florida CTE Courses & Certifications

Aligned with existing Florida CTE frameworks, enhancing current courses and industry certifications:

Florida DOE Curriculum Frameworks

Extends topics in AI Foundations (9401100), AI in the World (9401010), and Applications of AI (9401020). Can serve as a capstone.

Data Science & Machine Learning Programs

Complements courses like Procedural Programming, Data Analytics, and Machine Learning & Applications. Formalizes prompt engineering skills.

Industry-Recognized Certifications

Complements CIW AI Associate, CIW Data Science Specialist. Positions as a specialized credential for practical AI tool usage. Prepares for parts of Microsoft AI Fundamentals or IBM AI Engineering badges.

CTE Course Code Integration

Can be a new course code or embedded in existing courses (e.g., Foundations of Machine Learning). Meets CAPE required standards for IT and Engineering Technology.

This crosswalk ensures the program complements existing Florida CTE pathways and can be submitted for inclusion on Florida’s approved industry certification list.

Target Learner Levels

Designed for multiple learner levels in Florida’s education system, ensuring accessibility while maintaining rigor:

High School (Grades 9–12)

Ideal for 11th-12th grade CTE students in STEM/IT academies. Possible CTE elective credit. Suitable for upper-level students with some tech background.

Adult Technical Centers

Short-term courses (e.g., 300-hour program) for upskilling adults or career changers. Emphasizes practical workplace applications.

State Colleges (A.S. Programs)

College Credit Certificate (CCC) or integrated into A.S. degrees (Computer Programming, Data Science, Applied AI). Optional specialization.

Universities and AA Transfer

Prepares for university-level work in CS, Data Science. Signals readiness for undergraduate AI projects. Marketable in dual-enrollment.

Prerequisites and Support: Recommended intro computing course for high school. Baseline digital literacy and English proficiency for adult/college. Supports for different levels. Positioned at an intermediate level to broaden participation.

Stackability & Articulation Pathways

Designed to be stackable and articulated for college credit in Florida’s system (Rule 6A-10.0401):

High School College Credit

Guaranteed college credit (3-6 credits) via Gold Standard Articulation into related A.S. degrees (e.g., Applied AI, CIT).

Stackable College Credentials (CCC → A.S.)

Offered as a College Credit Certificate (CCC) that fully ladders into an Associate in Science degree. Students can earn the CCC and continue to a full A.S.

Pathways to Bachelor’s Degrees (A.S. → B.A.S./B.S.)

Content articulates into B.A.S./B.S. programs in data science, AI, or IT management. Can be recognized as Credit for Prior Learning.

Complementary Certifications

Stacks horizontally with other industry certs (Azure AI Fundamentals, Google Cloud AI Engineer). Fills gap by proving practical AI usage skills.

Lifelong Learning and Updates

Modular nature allows for updates or advanced add-on modules as AI evolves, maintaining articulation value.

This stackable design supports diverse career and education goals, meeting Florida’s Rule 6A-10.0401 for statewide articulation.

Industry Partnerships & Alignment

Strong industry partnerships ensure relevance to high-wage, high-growth workforce needs:

Advisory Council with AI Industry Experts: Input on curriculum, project scenarios, exam development.
Collaboration with AI Companies: Guest lectures, software credits, early access to new AI features (OpenAI, Google, Microsoft).
SmartTown.ai & Civic Innovation: Real-world case studies/projects (e.g., AI chatbot for citizen reporting).
Internships and Apprenticeships: Paid/credit-bearing internships with AI companies (e.g., GoodSAM model).
Employer-Led Challenges/Hackathons: Co-sponsored events for students to solve real AI problems.
Continuous Curriculum Input: Industry experts as adjuncts or co-developers for agile updates.

These partnerships ensure the certification addresses high-wage, high-growth job needs and cultivates ethical, socially responsible AI innovators.