Move past the hype to how AI actually learns.
How do computers write stories, generate photorealistic art, diagnose illnesses, or drive autonomous vehicles? In Artificial Intelligence and Machine Learning, a required elective, students uncover how modern AI systems learn, think, and create. The course blends foundational AI literacy with hands-on generative tools so students become both critical consumers and skilled creators.
What students will learn
- Core concepts of artificial intelligence, machine learning, and deep learning
- How generative AI systems (large language models and diffusion models) process and generate data
- Advanced prompt engineering: context setting, role prompting, iterative refinement, and few-shot prompting
- Hands-on multimodal creation: generating, modeling, and refining text, visuals, audio, and code
- AI literacy: recognizing hallucinations, evaluating model reliability, and detecting misinformation
- Ethics, safety, and societal impact: algorithmic bias, deepfakes, intellectual property, data privacy, and environmental considerations
- Real-world problem solving: integrating AI assistants into research, creative workflows, and digital products
- Practical testing, evaluation, and responsible deployment of AI solutions
Inside the classroom
Students design and optimize complex prompt workflows in hands-on design labs, audit existing AI models for bias and hallucinations, build functional prototypes using generative creation platforms, take on red-teaming and prompt-engineering challenges, and curate a final portfolio of AI-collaborative creative and technical projects.
Because this course uses cutting-edge artificial intelligence, students learn and practice responsible AI stewardship from day one. AI is treated as a thought partner and creative accelerator, not a replacement for student reasoning and critical thinking.
Ethical use and student disclosure
Transparency
Students disclose and document any generative AI assistance in their work, including the specific tool used, the prompts submitted, and how AI outputs were modified, verified, or integrated.
Human-in-the-loop accountability
Students keep full ownership of and accountability for their submitted projects, including verifying facts, mitigating algorithmic bias, and preventing hallucinations.
Privacy and safety
Students learn responsible data hygiene and commit never to input personal information, confidential records, or protected creative works into public AI models without authorization.
Authentic authorship
Undisclosed or uncredited AI-generated submissions are treated as violations of academic integrity, reinforcing that ethical literacy is as vital as technical skill.
Skills students build
Prompt engineering, AI literacy, critical thinking, ethical analysis, computational logic, creative storytelling, digital problem solving, iterative prototyping, and technical communication.
Where it can lead
AI prompt engineer, machine learning specialist, data analyst, AI ethicist and policy advisor, digital product manager, software developer, creative technologist, automation consultant, and emerging roles across every industry using generative AI.
The policy behind the course
This course sets the transparency and authorship rules students follow in every other class. How the whole school approaches AI.
Be part of Ohana's founding class.
Applications open December 15, 2026. Join the waitlist to hear first about info sessions, showroom visits, and scholarship guidance.