Using Artificial Intelligence

Fictional sample for demonstration. Silver Oak University and Prof. Quinn are fictional, used to showcase thecoursemaker.com. No real institution, course, or person is implied or endorsed. Real companies, tools, and products are named factually as the subject matter.

Course Description

Artificial intelligence is becoming a baseline workforce skill in every field, and this course teaches you how to use it well — effectively, critically, and responsibly. It is a practical, hands-on, build-things course, not a computer-science or machine-learning course: there is no coding or math prerequisite, and you do not need any technical background. You need curiosity and a willingness to question your own assumptions — and the AI's.

We move along a deliberate arc: the right mindset → how AI actually works (and where it fails) → prompting in real depth → simulations → working across voice, audio, images and documents → choosing the right tool → verifying AI output → four hands-on weeks building real automations in Claude Cowork → privacy, ethics, and the future of work, finishing with a capstone where you design, build, and document an AI-powered workflow that solves a real problem in your own academic or work life.

A through-line runs across every single week: the tool drafts, you judge. AI is confidently wrong often enough that verifying its output is a core skill, not an afterthought — so every week includes a moment where you deliberately catch the AI's mistakes (a made-up fact, a fake citation, sycophantic praise, a flawed plan, the wrong tool for the job) and improve on them. That habit is the course.

A note on what this course is not. It is not a "ChatGPT tips" listicle, and it is not a doom-or-hype seminar about AI and society. It is a skills course: by the end you will be measurably better at getting high-quality, well-verified work out of AI tools — and at knowing when not to trust them.

Learning Objectives

By the end of the course, you will be able to:

1. Explain what generative AI and large language models are, how they work at a conceptual level (next-token prediction, training, tokens, the context window), and their core capabilities and limits — including why AI can be "confidently wrong."
2. Apply effective prompting — conversation and asking for guidance, providing content, emphasis, meta-prompting, the structured-prompt components, examples (zero/one/few-shot), and simulations — to get high-quality results from an AI assistant.
3. Work across modalities (text, voice, audio, images, documents) and choose the right AI tool for a given job from the current tool landscape.
4. Critically evaluate and verify AI output — recognize hallucination, sycophancy, and bias, and run a reliable verification workflow.
5. Use Claude Cowork to build agentic workflows — projects, connected folders and file read/write, tasks, skills, connectors, and live artifacts.
6. Automate real tasks with scheduled tasks and dispatch, and operate cross-app workflows (computer use, Claude in Chrome, Claude in Excel) — using agentic tools safely.
7. Apply responsible-AI practices — data privacy, terms of service, content ownership / IP, bias and fairness, and academic & professional integrity — and build a personal ethical framework for the AI age.
8. Integrate AI into real academic and work tasks, adapt as tools evolve, and design, build, document, and verify an end-to-end AI-powered workflow that solves a genuine problem.

Student Learning Outcomes (SLOs)

• SLO A — AI fluency & prompt craft. Produce high-quality, well-verified results with AI across modalities and tools — through strong prompting, tool selection, and agentic workflows.
• SLO B — Responsible & critical AI use. Evaluate, verify, and use AI ethically and safely — catching hallucination and sycophancy, protecting privacy and IP, and acting with integrity.

Required Materials

There is no required textbook, and you will pay nothing for course materials. Readings, videos, and tool references are delivered as links to external resources posted in each weekly module. You will need:

• A laptop with a web browser and internet access (bring it to every session).
• A free account on at least one approved AI assistant — ChatGPT, Claude, Gemini, or Copilot (the free tier of any is sufficient for most of the course).
• Claude Cowork desktop, which is required for Weeks 11–14 and the capstone (the hands-on automation weeks). Setup is walked through in Week 11; the free/trial access available at that time is sufficient for the coursework.
• A free recording/transcription app for Week 7 (your phone's voice recorder plus a free transcription tool is fine).

You do not need to pay for any premium tier to complete the course, and you do not need any prior experience with these tools.

Grading

Your course grade is the weighted total of the groups below. Weights sum to 100%.

Attendance is tracked at every session but is not weighted (see the Attendance Policy).

Per-item points: quizzes 10 · discussions 20 · assignments 100 · AI Build Studios 50 · midterm & final 100 each.

Letter-Grade Scale

Late & Make-Up Policy

• Late penalty: 10% per day. Submitted work loses 10 percentage points of its earned score for each day (or part of a day) it is late.
• Quizzes, the Midterm, and the Final are time-bound. Make-ups are arranged only for documented emergencies — contact Prof. Quinn as early as possible, ideally before the due date.
• AI Build Studios build on the week's skill and are best done the same week; the automation Studios (W11–14) depend on having Claude Cowork set up, so start early.
• Practice exercises are ungraded and exist for your benefit; the late penalty does not apply to them.
• If something serious is getting in the way of your work, reach out early. It is almost always easier to arrange support before a deadline than to repair a grade after it.

AI-Use Policy

This course is unusual, and the policy is deliberately the reverse of most courses: AI is the subject, so you are required to use it on your coursework — and it is banned on the closed assessments. Read this carefully, because the meta-design is the point.

The core idea

You cannot become fluent with AI without using it constantly, and you cannot prove you understand the material if an AI takes your quiz for you. So the course splits cleanly:

• AI is required (and the whole point) on: the Lecture Tutorials, Discussions, Assignments, Practice Exercises, and the weekly AI Build Studios. These are where you build skill by working with AI — and, crucially, by catching and correcting its mistakes.
• AI is not permitted on: the Quizzes, the Midterm, and the Final. These are built from auto-gradable items and exist to confirm that you personally understand the concepts, terminology, capabilities and limits, prompting principles, tool choices, and ethics — the things you'll need in your head when the AI isn't there or isn't trustworthy.

Why you can't just paste AI output for credit

The Studios and assignments are engineered so that raw AI output won't earn the grade. Each one requires you to use the AI, then verify, critique, and improve what it produced — and the rubric awards points for your judgment, verification, and iteration, not for the AI's first draft. A student who pastes an unchecked answer will miss exactly the points the activity is designed to measure.

Approved tools

Use one of these approved AI assistants: ChatGPT, Claude, Gemini, or Copilot (free tiers are fine). The automation weeks (W11–14) and the capstone additionally require Claude Cowork desktop.

How AI shows up in the adaptive activities

Your Lecture Tutorials, Discussions, and Assignments are adaptive-learning activities you complete with an assistant, then submit the conversation share link plus the AI's summary or self-scored report. The share link is part of your submission — treat the conversation as your work, keep it on-topic, and do your own thinking. The weekly AI Build Studio is submitted as your build + your AI-critique write-up (what the AI got wrong and how you fixed it).

Integrity

Using AI as described here is encouraged and fully consistent with the integrity standard below. The violations are fabricating or doctoring a chat you submit, and using AI on the closed assessments (Quizzes, Midterm, Final). When in doubt, ask before you submit.

Attendance Policy

This is an in-person course built around live demonstrations, hands-on building, and think-pair-share — much of the learning happens in the room.

• Attendance is tracked at every session. It is not part of your weighted grade, but a strong attendance record is expected, and the hands-on build sessions are hard to reconstruct alone — consistent absence will show in your performance.
• Bring your laptop, arrive on time, and engage professionally with your classmates and instructor.
• If you must miss a session, notify Prof. Quinn in advance when possible and review the module materials to catch up. You remain responsible for any content, announcements, and due dates from a missed class.

Academic Integrity

You are expected to do your own work and to represent it honestly. In this course, "your own work" includes working with AI where the activity calls for it — but it never includes fabricating a chat transcript, misrepresenting AI output as your own unverified reasoning on the closed assessments, or using AI where it is prohibited (the Quizzes, Midterm, and Final). Cheating, plagiarism, and submitting another's work — human or AI — as your own where it is not permitted are violations of academic integrity and will be handled according to university policy. When in doubt about what is allowed, ask first. Holding to this standard is what makes your grade — and your degree — mean something.

Accessibility: Silver Oak University is committed to equal access. Students who need accommodations should contact the campus disability services office to arrange them; notify Prof. Quinn early in the term so supports can be in place. (Placeholder — institutions should insert their official accessibility, Title IX, and integrity statements here.)

Course Schedule — Fall 2026 (16 Weeks)

Term runs Aug 31 – Dec 18. Campus holidays: Labor Day (Sep 7), Veterans Day (Nov 11), Thanksgiving (Nov 26–27). Week 16 is reserved for finals. Dates are the Monday of each week.

Practice exercises and a Lecture Tutorial are part of every week's module; the table lists the graded touchpoints. Exam weeks (8 & 16) carry no weekly Studio. The schedule may be adjusted with advance notice; changes will be announced in the course.