QuantHub
QuantHub
SupportAI Foundations
Ethical, professional AI literacy across disciplines — a twelve-lesson course that takes learners from how AI actually works, through prompt engineering and tool selection, to responsible everyday use in professional and academic workflows.
AI literacy is now a core professional competency.
Every discipline — business, health, education, technology — now assumes its people can work with AI. But most learners meet AI through headlines and hot takes, leaving them swinging between hype and dismissal with no calibrated middle ground.
AI Foundations builds that middle ground. Learners start with how AI systems actually work — the mechanisms, the history, the structural reasons outputs can't be blindly trusted — then move to the applied skills: prompt engineering, tool selection, ethics and security, and responsible AI-assisted work.
Every concept is grounded in real-world case studies and embedded interactive quizzes, connecting abstract ideas to the business and academic problems learners will actually face.
Calibration, not hype
AI history's boom-and-winter pattern gives learners the perspective to evaluate any new AI claim — neither swept up nor dismissive.
Responsible use as a practiced skill
Data privacy, bias recognition, and verification discipline are trained as habits — not covered as a compliance afterthought.
Frameworks that outlast the tools
Learners evaluate AI tools with structured, multi-dimensional criteria — a skill that transfers no matter which products come and go.
Two units, twelve lessons — from mechanism to responsible practice
Unit 1 builds the foundation: how AI works, where it came from, and the applied skills of prompting, tool selection, and ethical use. Unit 2 tackles the practical realities of working with AI day to day — including critically evaluating AI-generated code.
AI Foundations
Business Challenges & AI Applications
The five business pressures that drove AI adoption — and how to read AI transformation claims with healthy skepticism.
How AI Systems Actually Work
The core traits and mechanisms behind AI — including why hallucination and bias are structural, not fixable bugs.
The History & Current State of AI
From the Turing Test through two AI winters to transformers — the hype-cycle perspective that calibrates every AI claim.
AI Use Cases in Professional Workflows
Where generative AI genuinely helps in research, analytics, and content creation — and where human judgment stays essential.
Selecting AI Tools
A multi-dimensional evaluation framework that replaces "which tool is best?" with evidence-based selection.
Basics of Prompt Engineering
The Role-Task-Context-Format framework plus zero-shot, few-shot, and chain-of-thought patterns for reliable outputs.
Ethics & Security of AI
The five core ethical risks, six bias types, and the data-handling habits that make AI use safe, fair, and defensible.
AI as Your Learning Partner
Using AI to explain, challenge, and quiz you — while keeping your own thinking in the driver's seat.
AI in Academic Workflows
Literature search, argument development, and writing feedback — with clear academic-integrity boundaries throughout.
Practical Considerations for AI Use
Why AI Tools Behave Differently
Training data, architecture, and temperature — why two tools answer the same prompt differently, and how to choose between them.
Vibe Coding: Evaluating AI-Generated Code
A read-test-verify workflow for AI-generated code — catching edge cases and security risks before you run anything.
Vibe Coding: The Cognitive Debt of AI Code
Recognizing when AI-assisted work is building code you can run but can't explain — and practices that keep you in command.
What learners walk away with
Practical, durable AI literacy — the understanding, skills, and judgment that transfer across tools, disciplines, and job roles.
A working model of how AI works — able to explain next-token prediction, and why hallucination and bias require permanent human oversight.
Historical calibration — perspective on AI's boom-and-winter cycles that protects against both excessive hype and excessive dismissal.
Prompt engineering fundamentals — structured prompting with Role, Task, Context, and Format, plus the patterns that make outputs reproducible.
Evidence-based tool selection — a weighted-criteria framework for choosing AI tools on accuracy, speed, cost, privacy, and fit.
Ethical and secure practice — recognizing bias, protecting sensitive data, and treating every AI output as a hypothesis to verify.
Responsible AI-assisted work — integrating AI into professional, academic, and coding workflows without surrendering understanding or integrity.
Ready to teach. Ready to learn.
An interdisciplinary curriculum designed to drop into a course, an onboarding program, or self-directed learning — no technical prerequisites.
12 Focused Lessons
About 30 minutes each — roughly 4.5 hours of total learning across two units.
Case-Driven
Real-world case studies and embedded quizzes connect every concept to practical problems.
Self-Paced & Online
Start anytime, from any browser — built to fit around class schedules and workdays.
Adaptive by Design
Accommodates diverse skill levels — from first exposure to learners refining existing AI habits.
Frequently asked questions
Bring AI Foundations to your learners
Talk with the QuantHub team about rolling out AI Foundations across your institution or organization — as a standalone course, a curriculum module, or the base layer of a full AI literacy program.