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AI Foundations

AI Foundations

AI Foundations builds ethical, professional AI literacy across disciplines. Students explore the history of AI, core concepts, machine learning basics, generative AI, and prompt engineering.
Real-world case study exercises
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Hours of applied AI learning
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What You'll Gain

Ready-to-Teach Curriculum:

Makes AI accessible across any discipline.

Practical Application:

Case-driven design connects abstract AI concepts to practical business applications.

Simplified Ethics:

Provides clear frameworks and discussion prompts to simplify teaching ethics in AI.

Adaptive Learning:

Ensures students with no prior AI background can keep up, while advanced learners stay challenged.

Course Modules

This course is comprised of the following modules. Each module is a task-based case study that requires students to prove they can apply the concepts they’ve learned.
Module 2367: Integrating AI into academic workflows

Explore how AI can support academic work while maintaining human judgment, responsibility, and critical evaluation.

  • AI as a support for drafting, analysis, planning, synthesis, and feedback
  • Using AI to strengthen learning rather than replace thinking
  • Human verification of AI outputs across academic tasks
  • Responsible use practices for accuracy, privacy, and accountability

Use AI as an analytical assistant to produce clear, grounded, and verifiable analysis.

  • Structured prompts with context, instructions, output format, and success criteria
  • Grounding AI analysis with actual data to reduce vague or generic outputs
  • Iterative prompting to drill down, pivot, synthesize, and refine analysis
  • Validation of AI-generated claims, calculations, assumptions, and conclusions

Use AI to analyze performance-related information while protecting privacy and tailoring results for different audiences.

  • Comparative analysis prompts with clear dimensions, time boundaries, and methods
  • Privacy-first practices such as redaction, anonymization, and pseudonymization
  • Performance summaries, visualizations, and reproducible outputs
  • Audience-specific framing for executives, managers, HR, or other stakeholders

Use AI to build structured project planning artifacts while validating outputs against real-world constraints.

  • Work breakdown structures, schedules, stakeholder communication plans, and risk registers
  • Persona, context, constraints, dependencies, and structural requirements in project prompts
  • Human validation of AI-generated timelines, dependencies, and risks
  • Synthesizing multiple AI-generated artifacts into a stakeholder-ready project plan

Use generative AI to support learning, exploration, practice, and reflection.

  • AI support for clarifying concepts, generating examples, and exploring ideas
  • Asking AI for explanations, practice questions, feedback, and alternative perspectives
  • Using AI as a thinking partner rather than a shortcut to answers
  • Critical evaluation of AI responses during the learning process

Explain how AI evolved over time and how historical patterns shape the current AI landscape.

  • Major AI milestones, including symbolic AI, expert systems, deep learning, transformers, and generative AI
  • Patterns of hype, breakthrough, disillusionment, and renewed progress
  • The role of hardware, data, and algorithms in AI development
  • How historical context helps evaluate current AI claims and limitations

Explain foundational prompt engineering techniques used to communicate effectively with AI systems.

  • The four-component prompting framework: Role, Task, Context, and Format
  • How clear instructions, specificity, examples, and constraints shape AI outputs
  • Prompting patterns such as zero-shot, few-shot, chain-of-thought, persona, and RAG
  • Prompt refinement strategies for improving quality, relevance, and usefulness

Identify how generative AI can transform professional workflows across research, analytics, content creation, and communication.

  • GenAI use cases for summarization, synthesis, drafting, pattern detection, and communication
  • Human-AI partnership models for professional work
  • How GenAI changes workflows from querying tools to directing intelligent assistants
  • The continued importance of human direction, validation, and ethical judgment

Explain why organizations adopt AI and how AI addresses persistent business challenges.

  • Business pressures such as data overload, inefficiency, scalability limits, disconnected decision-making, and personalization demands
  • AI as a response to workflow, decision-making, and productivity challenges
  • Differences between point-solution AI and workflow-integrated AI
  • Barriers to AI adoption, including data readiness, change management, governance, and hallucination risk

Identify ethical, privacy, and security risks that AI users must recognize and mitigate.

  • Core AI risks, including bias, privacy, accountability, transparency, and power imbalances
  • Responsible practices such as data minimization, anonymization, access control, and verification
  • Recognizing and mitigating bias in AI-generated outputs
  • Treating AI outputs as hypotheses that require independent checking

Use structured criteria to evaluate and select AI tools for specific tasks and workflows.

  • AI tool categories such as generation, analysis, transformation, automation, and reasoning
  • Specialist vs. generalist AI tool trade-offs
  • Evaluation criteria such as accuracy, speed, cost, privacy, ease of use, integration, and reliability
  • Evidence-based tool selection for specific workflow needs

A Look Inside the Learning Experience

Students learn through a variety of interactive materials and hands-on environments designed to build real-world skills.

Practical Learning Resources:

Content is designed to help students frame problems and structure their thinking.

Interactive Quizzes:

Questions are embedded in real-world scenarios, testing a student's ability to apply knowledge in context.

Practical Learning Resources:

For courses like Excel for Business Analytics, students work directly in a simulated environment to solve problems.

Want the Full Curriculum?

Download the complete course guide to explore every module, learning path, and skill outcome.

Download the Curriculum Guide

AI Skills Are No Longer Optional

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Don’t Just Take Our Word for It…

“QuantHub is an exceptional learning tool. The clear, modular design helps students move quickly while giving faculty valuable insight into their progress.”

Dr. Uma Gupta

Associate Professor USC Upstate

“QuantHub’s modules put faculty in the driver’s seat. They’re flexible, practical, and meet educators where they are in their AI journey.”

Shani Robinson

Senior Associate Dean, SHSU

“I thought the AI essentials were useful, given how large of a role they play in our lives”

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Chloe

Student at UA

“Our school was on the failing list. After using QuantHub, students were excited to see their Science ACT scores jump—it completely changed how they approached data in labs.”

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Destiny Langford

Tuscaloosa City Schools

“QuantHub is an easy resource to incorporate valuable lessons into each class. I don’t have to lesson plan around it, and it doesn’t require any extra work on my end.”

Hannah Adams

McAdory High School

“Since adopting QuantHub, I haven’t had a single student banging on my door saying ‘I can’t understand this.’ Previously, Excel questions consumed my office hours.”

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Greg

MIS Professor

“QuantHub has completely freed up my ability to do more in class. We spent a lot more time on AI this semester than we ever have before.”

Trent

MIS Professor

“This is way beyond what other companies in your space are doing.”

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Jim Mezzanotte

Moodle

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