Art-Integrated GenAI Literacy for Music and Theater Educators

A fully online, self-paced course that builds generative AI (GenAI) literacy through hands-on music and theater projects. Across two modules and eight lessons you will compose, produce, script, animate, and choreograph with GenAI tools while developing seven competencies: computational thinking, prompt engineering, problem solving, creativity and innovation, AI feedback evaluation, AI ethics literacy, and collaboration. Every lesson ends with a shareable digital artifact and a short written rationale.

Audience: Pre-service and in-service music and theater teachers; arts educators new to generative AI.
Duration: About 12 to 16 hours total. Each lesson takes 60 to 90 minutes. Self-paced.
Instructor: Idowu David Awoyemi, The University of Alabama

Welcome and How This Course Works

Dear participant,

Welcome to a learning experience where creativity meets technology. You are about to build generative AI (GenAI) literacy the way artists learn best: by making things. Over two modules and eight lessons you will compose songs, produce beats, score scenes, write and stage scripts, direct avatars, and choreograph movement, all with GenAI tools as your collaborators. Along the way you will learn to understand, evaluate, and ethically guide those tools rather than simply use them.

Why this course exists

The next generation of learners will grow up in an AI-driven world. As a future music or theater teacher, your ability to navigate, harness, and ethically guide the use of AI will shape how your students create. By building GenAI literacy now, you will:

• Inspire your future students to become creative digital citizens.
• Enhance your own teaching practice with current tools.
• Lead classroom innovation through technology-enhanced arts experiences.
• Understand the ethics, limits, and possibilities of AI in education.

How the course is organized

You may complete Module 1 and Module 2 in either order. If your time is limited, complete the module closest to your teaching area first, then return for the other.

What every lesson looks like

Each lesson follows the same rhythm so you always know what comes next:

1. Watch a short introductory video and read the lesson objectives.
2. Learn the key ideas behind the tool and the competency the lesson targets.
3. Do a step-by-step activity with the GenAI tool.
4. Create a shareable digital artifact (a file or a link).
5. Share the artifact in the lesson gallery with a rationale of no more than 75 words.
6. Check your understanding with a short knowledge check.
7. Reflect with two or three prompts at the end of the lesson.

What you need

• A laptop or desktop computer with a reliable internet connection and headphones.
• Free accounts for the tools listed on the "Tools, Accounts, and Setup" page.
• A place to store your artifacts (Google Drive, OneDrive, or a personal folder) so you can link to them.
• Curiosity and a willingness to make imperfect first drafts.

How you are assessed

This course is competency-based. There are no timed exams.

A score of 80% or higher earns a certificate of completion.

Getting help

Post course questions in the "Introduce Yourself" forum thread titled "Questions." For private matters, email the instructor at idawoyemi@crimson.ua.edu. Expect a reply within two business days.

Let's create. Let's innovate. Let's lead.

The Seven GenAI Competencies

This course is built around seven GenAI competencies. Each lesson targets two or three of them, and every artifact you make is evidence of growth in at least one. Keep this page open as a reference; the objectives in each lesson use these names.

The competency framework

Where the framework comes from

The competencies draw on three sources you may want to cite in your own work:

• Long and Magerko (2020) defined AI literacy as a set of competencies that let people critically evaluate AI, communicate and collaborate with it, and use it as a tool at home, at work, and online.
• The UNESCO (2024) AI competency framework for teachers organizes teacher AI competence into five dimensions: a human-centred mindset, ethics of AI, AI foundations and applications, AI pedagogy, and AI for professional development.
• Computational thinking follows Wing (2006): formulating problems and solutions so that a computer, or a person, can carry them out.

How to use the framework as a teacher

When you design the final project in Module 3, you will name the competency each activity builds. Try this habit now: after each lesson, ask yourself "Which competency did this artifact exercise, and how would I know a student had grown in it?"

Reflection

Before moving on, rate yourself from 1 (new to this) to 5 (could teach it) on each competency in the Pre-Course Self-Assessment. You will rate yourself again at the end of the course.

Tools, Accounts, and Setup

You will use a small set of browser-based GenAI tools. All of them have a free tier that is enough for this course. Set up the accounts before you start Module 1 so the lessons flow without interruption.

Tools by lesson

Account setup checklist

1. Create a free Suno account at suno.com. Confirm you can generate one short test clip.
2. Create a free Soundtrap account at soundtrap.com. Open a new project and confirm you can hear the loop library.
3. Create a free Animaker account at animaker.com. Open a blank project and add one character.
4. Create a free invideo AI account at invideo.io. Generate one ten-second test clip.
5. Confirm you can sign in to at least one general-purpose text model (ChatGPT, Claude, or Gemini).
6. Create a folder named "GenAI Literacy Artifacts" in Google Drive or OneDrive. Set sharing to "Anyone with the link can view" so peers can open your gallery submissions.

File formats and naming

Save artifacts with the pattern Lesson-1-1_LastName_Title.mp3 (or .mp4, .pdf). Accepted formats:

• Audio: MP3 or WAV
• Video: MP4 or a shareable link (unlisted YouTube, Drive, or the tool's own share link)
• Scripts and rationales: PDF or Google Doc link

Accessibility notes

• All tools in this course work with keyboard navigation and screen readers to a usable degree, but Soundtrap's timeline is the hardest to use without a mouse. If you need an alternative, BandLab's grid editor is more accessible.
• When you share a video, add captions. invideo AI and Animaker both generate captions automatically; check them for accuracy.
• If a tool is blocked on your school network, complete the lesson with the free alternative listed above. The competencies are tool-agnostic.

Troubleshooting

• The tool asks me to pay. Every lesson can be completed on the free tier. Shorten the output length or reduce the number of generations.
• My generation sounds or looks wrong. That is expected and is part of the learning. Lesson 1.3 and 2.4 teach you how to evaluate and fix outputs.
• I cannot share my file. Upload it to your artifacts folder and paste the sharing link in the gallery post.

Using AI Responsibly in This Course

Generative AI is a creative collaborator in this course, not a shortcut around your own thinking. This page sets the expectations you will follow here, and it models the kind of policy you may later write for your own classroom.

What you may do

• Use GenAI tools to generate, remix, and iterate on music, scripts, animation, and video for every lesson activity. That is the point of the course.
• Use a text model to brainstorm, outline, or critique your rationales and reflections.
• Share AI-generated artifacts with peers in the galleries, as long as you label them.

What you must do

1. Disclose. Every artifact you share carries a one-line credit that names the tool, the version if shown, and what you did yourself. Example: "Melody and vocals generated in Suno v4 from my lyrics; arrangement edited and mixed by me in Soundtrap."
2. Write your own rationale and reflections. These are the evidence of your thinking. You may use a text model to check clarity, but the ideas and the wording must be yours.
3. Respect people. Do not clone the voice, likeness, or signature style of a real, named artist or a real person you know. Do not upload other people's recordings or images without permission.
4. Check before you trust. When a model gives you a fact (a composer's dates, a theater convention, a copyright rule), verify it before you repeat it.
5. Protect data. Do not enter student names, grades, or health information into any tool.

What you must not do

• Submit an artifact you did not make or shape yourself.
• Present AI-generated text as your own reflection.
• Use tools to produce content that demeans a group of people or depicts real individuals in sexual, violent, or defamatory ways.

Why this matters in the arts

Musicians and actors have a direct stake in how GenAI is trained and used. Many models were trained on recordings and performances without artists' consent, and the legal landscape is unsettled. Lessons 1.4 and 2.4 examine these questions in depth. For now, adopt the working stance of this course: use the tools, credit the tools, and keep the human artist visibly in the loop.

Your turn

Copy the one-line credit format above into a note. You will paste a version of it under every artifact you share.

Introduce Yourself: Your Art, Your Questions

Welcome to the course community. Start a new thread with your name as the title and tell us:

1. Your art form (music, theater, or both) and the grade levels you hope to teach.
2. One thing you have already made or tried with a GenAI tool, or, if you have not, one thing you are curious about.
3. One question or worry you have about AI in the arts classroom.

Then reply to at least two classmates. Look for someone whose question you can answer, or whose worry you share.

A thread titled Questions is pinned at the top. Post course logistics questions there so everyone benefits from the answer.

Pre-Course Self-Assessment

This is not a test. Rate your current confidence honestly so you can see your growth at the end of the course. Ungraded; one attempt.

Music Module Introduction

Dive into sound creation, loop-based producing, beat making, and digital composition using GenAI tools. In this module you will learn how AI can amplify your musical creativity while you build the competencies that let you evaluate and guide it.

What you will make

Before you start

• Complete the account setup for Suno and Soundtrap.
• Put on headphones. Many differences you will be asked to hear (tempo drift, muddy low end, robotic vocal phrasing) are hard to notice on laptop speakers.
• Keep a running document titled "Music Module Notes." Paste every prompt you use and one line about what it produced. You will need it for the module reflection.

A note on musicianship

Nothing in this module replaces what you know about melody, harmony, rhythm, and form. The tools are fast and often impressive, and they are also frequently wrong about the things a trained musician notices first. Your expertise is what turns a generation into a composition. Every lesson asks you to bring that expertise to bear.

Estimated time

Four to six hours across the four lessons. Each lesson takes 60 to 90 minutes including the gallery post and knowledge check.

Lesson 1.1: Composing an Original Song with GenAI

Estimated time: 75 minutes. Competencies: prompt engineering, creativity and innovation.

Learning objectives

By the end of this lesson you will be able to:

1. Write a structured song prompt that specifies genre, tempo, mood, instrumentation, vocal style, and form.
2. Generate at least three versions of a song from your own lyrics and select one using musical criteria.
3. Explain, in a 75-word rationale, which prompt choices produced the strongest result and why.

Warm-up: listen like a producer (5 minutes)

Play any recorded song you know well. While it plays, write down six things a producer had to decide: tempo, key or mode, instrumentation, vocal style, form (verse, chorus, bridge), and mood. These six decisions are exactly what a text-to-song model needs from you. If you leave them out, the model decides for you.

Key ideas

A prompt is a brief, not a wish

Text-to-song tools such as Suno take two inputs: a style prompt (a short description of how the song should sound) and lyrics (the words, with structure tags such as [Verse] and [Chorus]). Vague prompts ("a nice pop song") produce generic output. Specific prompts ("upbeat indie pop, 118 BPM, bright acoustic guitar and handclaps, warm female alto vocal, sparse first verse building to a full chorus") give the model constraints to work within, and constraints are where style lives.

The five-part prompt frame

Use this frame for every music prompt in the course:

Structure tags do the sequencing

Lyrics with tags such as [Intro], [Verse 1], [Pre-Chorus], [Chorus], [Bridge], and [Outro] tell the model where sections begin. Without them, choruses land in strange places. Tags are your first taste of computational thinking: you are sequencing the form so the model can follow it.

Iterate on purpose

Generation is cheap; judgment is not. Change one variable per generation so you can hear what it did. A useful order: get the form right first, then the tempo and feel, then the instrumentation, and finally the vocal.

Activity: compose your song (45 minutes)

1. Write lyrics for a short song: two verses and a chorus, optionally a bridge. Keep it under 16 lines. Choose a topic connected to teaching, your community, or a piece you already love. Write in your own words; do not ask a text model to write the lyrics for this lesson.
2. Tag the structure with [Verse 1], [Chorus], [Verse 2], [Chorus], [Bridge], [Chorus].
3. Write a style prompt using the five-part frame. Keep it under 200 characters if the tool limits length.
4. Generate version 1 in Suno with custom mode on, pasting your lyrics and style prompt.
5. Listen critically. Fill in the evaluation grid below.
6. Change one variable and generate version 2. Repeat for version 3. Record what you changed each time in your Music Module Notes.
7. Select the strongest version. Download it as MP3 and name it Lesson-1-1_LastName_Title.mp3.

Evaluation grid

Prompt bank

Adapt any of these as starting points:

• "Gospel-influenced pop, 96 BPM, piano and choir, powerful female lead, builds to a big final chorus, hopeful"
• "Lo-fi hip hop, 82 BPM, dusty drums and vinyl crackle, soft male spoken-sung vocal, mellow and reflective"
• "Afrobeats, 104 BPM, log drum and shaker, bright synth chords, playful call-and-response vocals, celebratory"
• "Folk ballad, 3/4 time, fingerpicked guitar and cello, gentle alto, wistful, ends quietly"

Create and share

Post in the Lesson 1.1 Gallery:

• Your selected MP3 or a share link.
• Your final lyrics with tags.
• Your final style prompt.
• A rationale of no more than 75 words: which prompt choices produced the strongest result and why.
• Your one-line AI credit (see "Using AI Responsibly").

Resources

• Suno help center: how custom mode and structure tags work.
• Any recording you used in the warm-up, as a reference for arrangement.
• Optional: Boomy or Udio, if Suno is unavailable on your network.

Reflection prompts

Answer in your Music Module Notes; you will draw on them for the module reflection.

1. Which of the five prompt parts made the biggest audible difference?
2. Where did the tool ignore your instructions, and how did you respond?
3. If a student handed you this song, what feedback would you give on the lyrics versus the production?

Lesson 1.1 Gallery: Share Your Song

Share the song you composed in Lesson 1.1. Start a new thread titled with your song's name and include:

• The MP3 file or a share link.
• Your final lyrics with structure tags.
• Your final style prompt.
• A rationale of no more than 75 words: which prompt choices produced the strongest result and why.
• Your one-line AI credit.

Then listen to two classmates' songs and reply to each with one specific thing the prompt did well and one variable you would change on the next generation. Refer to the five-part prompt frame in your feedback.

Lesson 1.1 Knowledge Check

Five questions on prompt engineering for text-to-song tools. Unlimited attempts; your best score counts.

Lesson 1.2: Beat Making and Loop-Based Production

Estimated time: 75 minutes. Competencies: computational thinking, collaboration.

Learning objectives

By the end of this lesson you will be able to:

1. Decompose a beat into layers (kick, snare, hi-hat, bass, chords, melody) and describe each as a repeating pattern.
2. Build a 16-bar beat in a loop-based digital audio workstation using pattern, repetition, and variation.
3. Use an AI-assisted feature (loop recommendation, pattern generation, or automatic mastering) and explain what it decided for you.
4. Give a peer structured feedback on the arrangement of their beat.

Warm-up: clap the pattern (5 minutes)

Clap this pattern four times: 1 (rest) and (rest) 3 (rest) and (rest). Now say "kick" on 1 and 3 and "snare" on 2 and 4 while clapping eighth notes with your other hand. You just held three simultaneous patterns in your head. A beat maker does the same thing, but writes each pattern down so a machine can repeat it exactly.

Key ideas

Beats are patterns, and patterns are code

Computational thinking has four moves: decomposition (break the problem into parts), pattern recognition (find the repeats), abstraction (keep the essential, drop the rest), and algorithm design (write the steps). A drum loop is all four at once. You decompose the groove into instruments, notice that the hi-hat repeats every eighth note, abstract the groove into a grid, and sequence bars into a form.

The grid

Loop-based tools such as Soundtrap show time as a grid: bars across the top, instruments down the side. One bar of 4/4 has 16 sixteenth-note cells. A kick on cells 1 and 9, a snare on cells 5 and 13, and hi-hats on every odd cell is the most common pattern in popular music. Everything else is variation on it.

Form is repetition with variation

A 16-bar beat is not sixteen different bars. It is usually one four-bar phrase repeated four times with small changes: a fill in bar 4, a dropped kick in bar 8, a new element at bar 9, a pause before bar 13. Listeners hear the repetition as stability and the variation as interest. Sequencing those changes is algorithm design.

Where the AI is

Modern workstations embed AI in several places: loop recommendation (the tool suggests loops that fit your key and tempo), pattern generation (the tool proposes a drum pattern in a chosen style), and automatic mastering (the tool balances levels and loudness). Each is a decision made for you. Your job in this lesson is to use at least one, notice what it decided, and decide whether you agree.

Activity: build a 16-bar beat (50 minutes)

1. Set up. Open a new Soundtrap project. Set the tempo (try 90 BPM for hip hop, 120 for house, 105 for Afrobeats) and choose a key.
2. Layer 1: drums. Add a drum track. Using the pattern editor or a loop, build a four-bar drum pattern. Write the pattern on paper first as a grid of Xs; that is your decomposition.
3. Layer 2: bass. Add a bass loop or play a simple two-note bassline that repeats every bar.
4. Layer 3: chords or melody. Use the tool's loop recommendation to find a chord or melody loop in your key. Note what it recommended and why you accepted or rejected it.
5. Extend to 16 bars. Duplicate the four-bar phrase three times.
6. Add variation. Make at least four deliberate changes: a fill at the end of bar 4, remove the bass in bar 8, introduce a new element at bar 9, and create a one-beat silence before bar 13. Write these changes as a numbered list; that list is your algorithm.
7. Use one AI feature. Try automatic mastering or pattern generation. Listen with and without it. Record what changed.
8. Export as MP3 and name it Lesson-1-2_LastName_Title.mp3.

Pattern worksheet

Copy this grid into your notes for each drum instrument. Mark an X where the instrument plays.

Create and share

Post in the Lesson 1.2 Gallery:

• Your MP3 or share link.
• Your pattern grid for the drums (a photo of paper is fine).
• Your numbered list of the four variations.
• A rationale of no more than 75 words: what the AI feature decided and whether you kept it.
• Your one-line AI credit.

Reply to two peers using the feedback frame: "I noticed [specific pattern or variation]. It works because [musical reason]. At bar [n] I would try [specific change]."

Resources

• Soundtrap help: pattern beatmaker and loop library.
• Optional: BandLab or GarageBand as alternatives.
• Wing (2006) on computational thinking, for the definitions used above.

Reflection prompts

1. Which of the four computational thinking moves felt most natural, and which was hardest?
2. What did the AI feature change that you would not have chosen yourself?
3. How would you teach the pattern grid to a middle-school general music class without a computer?

Lesson 1.2 Gallery: Share Your Beat

Share the 16-bar beat you built in Lesson 1.2. Start a new thread titled with your beat's name and include:

• The MP3 or share link.
• Your drum pattern grid (a photo of paper is fine).
• Your numbered list of the four variations you added.
• A rationale of no more than 75 words: what the AI feature decided and whether you kept it.
• Your one-line AI credit.

Reply to two peers with the feedback frame: "I noticed [specific pattern or variation]. It works because [musical reason]. At bar [n] I would try [specific change]."

Lesson 1.2 Knowledge Check

Five questions on computational thinking in loop-based production. Unlimited attempts; your best score counts.

Lesson 1.3: Scoring a Scene with AI-Generated Soundtracks

Estimated time: 90 minutes. Competencies: problem solving, AI feedback evaluation.

Learning objectives

By the end of this lesson you will be able to:

1. Translate a scene's dramatic beats into a cue sheet with mood, tempo, and instrumentation targets.
2. Generate instrumental cues with a GenAI tool and evaluate each against the cue sheet using explicit criteria.
3. Diagnose a mismatched cue and apply one of three fixes: revise the prompt, change the tool, or edit the audio.
4. Assemble a scored scene of 30 to 60 seconds with two contrasting cues.

Warm-up: what does the music know? (5 minutes)

Watch 30 seconds of any film scene with the sound off. Write down what you think is happening emotionally. Now watch it with the sound on. Did the score confirm your reading, add something, or contradict it? Film composers call the last option "playing against the scene." You will make that choice deliberately today.

Key ideas

Scoring starts with a cue sheet

A cue sheet lists each moment that needs music, when it starts and ends, and what it should do. Before you touch a tool, you decide: does this cue establish a place, reveal a feeling, build tension, or release it? A cue sheet turns a vague request ("make it sad") into a brief the model can act on and a standard you can evaluate against.

Evaluating AI output is a skill, not a reaction

"I like it" is not evaluation. Evaluation compares the output with the brief on named criteria. For a cue, the criteria are:

Rate each from 1 to 4. Anything at 1 or 2 is a problem to solve, not a reason to give up.

Three fixes, in order

When a cue misses, work through these in order:

1. Revise the prompt. Most misses come from underspecified mood or tempo. Add a reference ("in the style of a slow-burning noir cue"), a tempo, and a shape ("starts sparse, swells at 20 seconds").
2. Change the tool or mode. If the model keeps adding vocals, switch to instrumental mode. If it cannot do the genre, try Soundraw or Mubert, which are built for mood-based cues.
3. Edit the audio. Trim, fade, layer, or slow the cue in Soundtrap. Sometimes the best fix is a 12-second crossfade you make yourself.

Diagnosing which fix applies is the problem-solving competency in action.

Activity: score a scene (60 minutes)

1. Choose a scene. Use a 30 to 60 second clip you have the right to use: a public-domain film clip, your own rehearsal footage without identifiable students, or a silent animatic you build from three still images in Canva.
2. Write the cue sheet. Identify two contrasting cues. Example: Cue A (0:00 to 0:25) establishes a calm kitchen at dawn; Cue B (0:25 to 0:50) shifts to dread as a letter is opened. For each cue write mood, tempo, instrumentation, and shape.
3. Generate Cue A. In Suno's instrumental mode (or Soundraw), write a prompt from the cue sheet. Generate two candidates. Score both on the six criteria.
4. Generate Cue B. Repeat. Make sure Cue B contrasts with Cue A in at least tempo and instrumentation.
5. Diagnose and fix. Pick the weakest score across your four candidates. Apply one of the three fixes and regenerate or edit. Record the diagnosis and the fix in your notes.
6. Assemble. In Soundtrap or a video editor (Canva, CapCut, iMovie), place the clip on the timeline, add Cue A and Cue B, and create a transition between them (a crossfade, a hard cut on a visual beat, or a moment of silence).
7. Export as MP4 and name it Lesson-1-3_LastName_Title.mp4. If you cannot export video, export the two cues as MP3 and submit them with the cue sheet.

Prompt bank for cues

• "Instrumental, cinematic ambient, 60 BPM, soft piano and warm string pad, gentle and hopeful, slow swell over 30 seconds"
• "Instrumental, tense underscore, 84 BPM, low cello drone, ticking percussion, rising dissonant strings, no melody"
• "Instrumental, playful chamber cue, 132 BPM, pizzicato strings and clarinet, light and mischievous, ends on a question"
• "Instrumental, West African kora and percussion, 100 BPM, bright and communal, steady groove"

Create and share

Post in the Lesson 1.3 Gallery:

• Your scored scene (MP4 or link), or two cues plus the cue sheet.
• Your cue sheet.
• Your scores for all four candidates on the six criteria.
• A rationale of no more than 75 words: the cue you diagnosed, the fix you chose, and why.
• Your one-line AI credit.

Reply to two peers: name one cue that works, and one moment where you would have "played against the scene."

Resources

• Suno instrumental mode; Soundraw and Mubert as mood-based alternatives.
• Public-domain film sources: the Prelinger Archives on archive.org.
• Canva, CapCut, or iMovie for assembly.

Reflection prompts

1. Which criterion did the AI fail most often, and what does that tell you about how it was trained?
2. When was editing the audio yourself faster than regenerating?
3. How would you use the six-criteria grid as a rubric for a student scoring project?

Lesson 1.3 Gallery: Share Your Scored Scene

Share the scene you scored in Lesson 1.3. Start a new thread titled with your scene's name and include:

• The scored scene (MP4 or share link), or your two cues as MP3 plus the cue sheet.
• Your cue sheet with mood, tempo, instrumentation, and shape for each cue.
• Your six-criteria scores for all four candidate cues.
• A rationale of no more than 75 words: the cue you diagnosed as weakest, the fix you chose (revise prompt, change tool, or edit audio), and why.
• Your one-line AI credit.

Reply to two peers: name one cue that works and say why, and identify one moment where you would have played against the scene.

Lesson 1.3 Knowledge Check

Five questions on evaluating and fixing AI-generated cues. Unlimited attempts; your best score counts.

Lesson 1.4: Voice, Ownership, and Ethics in AI Music

Estimated time: 75 minutes. Competencies: AI ethics literacy, AI feedback evaluation.

Learning objectives

By the end of this lesson you will be able to:

1. Explain four ethical questions raised by GenAI in music: training data and consent, voice and likeness, ownership and copyright, and disclosure.
2. Analyze a real case using a structured ethics framework and take a defensible position.
3. Write an attribution statement for an AI-assisted musical work that names the human and machine contributions.
4. Evaluate a text model's account of a legal or ethical question and identify where it is uncertain, outdated, or wrong.

Warm-up: whose song is it? (5 minutes)

Think about the song you made in Lesson 1.1. List everyone and everything that contributed to it: you, the model, the people whose recordings trained the model, the company that built it, the tool that mixed it. Now ask: who should be credited, who should be paid, and who was asked? Keep the list; you will return to it.

Key ideas

Four questions every AI music project raises

1. Training data and consent. Text-to-music models learn from large collections of recordings. In many cases the artists whose work was used were not asked and were not paid. Some companies have licensing deals; others rely on arguments about fair use that courts are still testing. As an educator, you do not have to settle the law, but you do have to be honest with students that the tool's fluency rests on other people's work.

2. Voice and likeness. Models can imitate a recognizable singer's voice. Several jurisdictions treat a person's voice as part of their right of publicity, and some have passed laws specifically about AI voice cloning. The course rule is simple: do not clone the voice or signature style of a real, named artist. Prompt for a type of voice ("husky tenor"), not a person.

3. Ownership and copyright. In the United States, the Copyright Office has stated that works generated entirely by AI without human authorship are not eligible for copyright, while works with meaningful human creative contribution can be, to the extent of that contribution. That means your lyrics and your arrangement choices may be protectable; the raw generation may not be. Tools' terms of service also differ on who may use outputs commercially, and free tiers often restrict this. Read the terms before you release anything.

4. Disclosure. Audiences, students, and collaborators deserve to know when AI was involved. Disclosure protects trust and models good practice. It is the reason for the one-line credit under every artifact in this course.

A framework for taking a position

When you analyze a case, work through these steps and write a sentence for each:

Text models are not legal counsel

Later in this lesson you will ask a text model to summarize a legal question. Expect a confident, fluent answer that may be out of date or simply wrong. Models are trained on text up to a cutoff, they blend jurisdictions, and they sometimes invent case names. Treat the answer as a first draft to verify, and note every claim you cannot confirm from a primary source (a court filing, a government office, the tool's own terms).

Activity: analyze a case and write your attribution (55 minutes)

1. Choose a case. Pick one: - A 2023 viral track used AI-cloned voices of two major recording artists and was removed from streaming platforms after their label objected. - A record label group sued two text-to-music companies in 2024, alleging their models were trained on copyrighted recordings without a license. - A music teacher used an AI tool to generate an accompaniment track for a school concert program and listed it as "arranged by" the teacher.
2. Research the facts. Find two sources: one news report and one primary source (a court filing, a statement from the Copyright Office, or the tool's terms). Note the dates.
3. Ask a text model to summarize the legal and ethical issues in the case. Paste the answer into your notes.
4. Evaluate the model's answer. Mark each claim as confirmed, unconfirmed, or wrong based on your two sources. Count how many fall in each category.
5. Apply the framework. Write one sentence for each of the six steps. End with your position.
6. Write your attribution statement for the song from Lesson 1.1, using this template and adapting it:

"Lyrics written by [name]. Melody, arrangement, and vocals generated with [tool and version] from those lyrics and a style prompt written by [name]. Final mix edited by [name] in [tool]. This work includes AI-generated material and is shared for educational use."

1. Save the case analysis and attribution as a PDF named Lesson-1-4_LastName_Ethics.pdf.

Create and share

Post in the Lesson 1.4 Forum:

• Your case analysis PDF.
• Your attribution statement.
• The count of confirmed, unconfirmed, and wrong claims in the text model's summary, with one example of each.
• A rationale of no more than 75 words: the rule you would set for AI music in your classroom, and why.
• Your one-line AI credit.

Then reply to two peers who took a different position or analyzed a different case. Engage with their reasoning, not just their conclusion.

Resources

• United States Copyright Office guidance on works containing AI-generated material (search "Copyright Office AI policy guidance").
• Your chosen tool's terms of service, especially the sections on ownership and commercial use.
• UNESCO (2024) AI competency framework for teachers, dimension 2: ethics of AI.

Reflection prompts

1. Which of the four questions do you think your future students will care about most, and why?
2. What did the text model get wrong, and how would you have known without your sources?
3. Has this lesson changed how you feel about the song you made in Lesson 1.1?

Lesson 1.4 Forum: The Attribution Debate

This forum hosts the attribution debate for Lesson 1.4. Start a new thread titled with the case you analyzed and include:

• Your case analysis PDF.
• Your attribution statement for the song from Lesson 1.1.
• The count of confirmed, unconfirmed, and wrong claims in the text model's summary, with one example of each.
• A rationale of no more than 75 words: the rule you would set for AI music in your classroom, and why.
• Your one-line AI credit.

Reply to two peers who took a different position or analyzed a different case. Engage with their reasoning: which stakeholder did they weigh most heavily, and would you weigh it the same way?

Lesson 1.4 Knowledge Check

Five questions on ethics and attribution in AI music. Unlimited attempts; your best score counts.

Music Module Reflection

Write a reflection of 500 to 750 words on your work across the four music lessons. Use your Music Module Notes: the prompts you saved, the evaluation grids, and the reflection prompts you answered after each lesson.

Prompts to address

Address all four; use headings so the reader can find each one.

1. Growth. Choose two of the seven competencies. For each, describe one specific moment in the module where you exercised it and what you would do differently now.
2. The tool and you. Where did the AI tools add something you could not have made alone, and where did your musicianship correct or override them? Give one concrete example of each.
3. Ethics in practice. Restate the classroom rule you proposed in Lesson 1.4. Has working through the whole module changed it? Why or why not?
4. Transfer. Pick one of the four artifacts. Describe how you would adapt the activity for a specific grade level and class you might teach, including one modification for a student with limited access to technology at home.

Submission

Submit a PDF or a Google Doc link. Include your name, the module, and the date at the top. Write in first person. You may use a text model to check clarity, but the reflection must be your own writing; a reflection that reads as AI-generated will be returned unmarked.

Rubric (20 points)

Theater Module Introduction

Step into digital storytelling, avatar-based performance, and drama-dance through AI. In this module you will experience how technology can transform theatrical expression while you build the competencies that let you evaluate and direct the tools rather than be directed by them.

What you will make

Before you start

• Complete the account setup for Animaker and invideo AI, and confirm access to a text model.
• Keep a running document titled "Theater Module Notes." Paste every prompt you use and one line about the result.
• If you completed the Music module, notice how often the same moves return: the five-part brief, one variable per iteration, and the six-criteria evaluation. Theater changes the vocabulary, not the method.

A note on the live art

Theater happens between people in a room. Nothing here replaces that. The tools in this module help you draft faster, visualize staging before you have a cast, rehearse without a partner, and give students a way to see their ideas move. Every lesson asks how the artifact returns to the rehearsal room.

Estimated time

Four to six hours across the four lessons. Each lesson takes 60 to 90 minutes including the gallery post and knowledge check.

Lesson 2.1: AI-Assisted Dramatic Storytelling

Estimated time: 75 minutes. Competencies: prompt engineering, creativity and innovation.

Learning objectives

By the end of this lesson you will be able to:

1. Write a scene brief that specifies characters, objective, obstacle, setting, tone, and form.
2. Use a text model to draft a scene and then direct at least two rounds of revision with specific dramaturgical notes.
3. Rewrite at least a third of the dialogue in your own voice and explain what you changed and why.
4. Prepare the scene for a table read.

Warm-up: the smallest scene (5 minutes)

Write three lines of dialogue between two characters who want opposite things. Do not name the things; let the wanting show in the lines. This is the core of dramatic writing: objective meets obstacle. A text model can produce fluent dialogue instantly, but it does not know what your characters want unless you tell it.

Key ideas

The scene brief

A text model is a fast, tireless, slightly bland writing partner. Its first draft will be competent and forgettable. What makes it useful is a brief that carries your artistic choices. Use this frame:

Prompting is directing

After the first draft, your prompts become director's notes. Good notes are specific and actionable: "Cut the exposition in the first six lines; start with her already holding the trumpet case." "Give him one line where he almost agrees, then retreats." "Replace the final speech with a single gesture." Vague notes ("make it better," "more emotional") produce vague rewrites.

The human rewrite

A scene is finished when it sounds like people you know. Models default to characters who say what they mean and explain their feelings. Real people deflect, interrupt, joke at the wrong moment, and leave things unsaid. Your rewrite is where the scene becomes yours: cut explanation, add subtext, and give each character one verbal habit.

Activity: draft, direct, rewrite (55 minutes)

1. Write the brief using the six-element frame. Choose a situation connected to a school, a rehearsal, a family, or a community you know.
2. Draft. Paste the brief into a text model with this instruction: "Write a two-page, two-character scene in standard stage-play format based on this brief. Use only dialogue and brief stage directions."
3. Read it aloud (both parts). Mark every line that explains a feeling instead of showing it, and every line that no real person would say.
4. Direct round 1. Give the model three specific notes drawn from your marks. Ask for a revised draft.
5. Direct round 2. Give three more notes. Focus on the ending and on subtext.
6. Rewrite by hand. Copy the scene into a document. Rewrite at least one third of the lines yourself. Track your changes or highlight them.
7. Prepare for a table read. Add a title, a character list with one-line descriptions, and a one-sentence note to actors about tone.
8. Save as PDF named Lesson-2-1_LastName_Title.pdf.

Note bank

Director's notes you can adapt for revision rounds:

• "Start the scene later: cut the first [n] lines."
• "Give [character] a line they regret immediately."
• "Replace every line that names an emotion with an action."
• "Add one interruption where they talk over each other."
• "End on an image, not a statement."
• "Make the setting do work: have a prop from the setting change hands."

Create and share

Post in the Lesson 2.1 Gallery:

• The scene PDF with your rewrites marked.
• Your six-element brief.
• The six director's notes you gave across the two rounds.
• A rationale of no more than 75 words: which note produced the biggest change, and what your rewrite added that the model could not.
• Your one-line AI credit.

Reply to two peers: quote one line you would keep exactly as written, and give one director's note for the next revision.

Resources

• Any text model (ChatGPT, Claude, Gemini).
• A standard stage-play format guide (search "Dramatists Guild formatting").
• Optional: a peer or family member to read the second part aloud with you.

Reflection prompts

1. Where did the model's draft surprise you, and where was it predictable?
2. Which of your rewrites would you defend to a student who preferred the model's version?
3. How would you structure this activity so students cannot skip the rewrite step?

Lesson 2.1 Gallery: Share Your Scene

Share the scene you drafted, directed, and rewrote in Lesson 2.1. Start a new thread titled with your scene's name and include:

• The scene PDF with your rewrites marked.
• Your six-element brief (characters, objective, obstacle, setting, tone, form).
• The six director's notes you gave across the two revision rounds.
• A rationale of no more than 75 words: which note produced the biggest change, and what your rewrite added that the model could not.
• Your one-line AI credit.

Reply to two peers: quote one line you would keep exactly as written, and give one director's note for the next revision.

Lesson 2.1 Knowledge Check

Five questions on prompting and directing a text model for dramatic writing. Unlimited attempts; your best score counts.

Lesson 2.2: Avatar-Based Performance

Estimated time: 90 minutes. Competencies: computational thinking, collaboration.

Learning objectives

By the end of this lesson you will be able to:

1. Score a monologue or scene excerpt into beats and map each beat to a performance decision (gesture, expression, position, camera).
2. Build an avatar-based performance of 45 to 90 seconds that sequences those decisions in an animation tool.
3. Use AI voice or lip-sync features and evaluate whether the delivery serves the text.
4. Co-review a peer's performance using a shared beat sheet.

Warm-up: the beat sheet (5 minutes)

Take any twelve-line speech. Draw a line wherever the speaker's tactic changes: from pleading to threatening, from remembering to deciding. Each segment is a beat. Directors and actors score scripts this way; animators need the same thing, because every beat becomes a change the software must be told to make.

Key ideas

Animation is performance, decomposed

On stage, an actor makes hundreds of micro-decisions continuously. In an animation tool, nothing happens unless you specify it. That forces the computational thinking moves into the open: decompose the speech into beats, recognize the pattern of shifts, abstract each beat into a few controllable variables, and sequence them on a timeline. Students who struggle to articulate what an actor "does" often find it easier when they must tell a character to do it.

The four variables

For each beat you control four things in most animation tools:

Where the AI is

Tools such as Animaker and Synthesia offer AI text-to-speech, automatic lip-sync, and, in some, automatic gesture suggestion. They make the avatar look alive with almost no input, which is exactly the problem: default delivery is even, pleasant, and dramatically empty. Your beat sheet is what makes the performance mean something. Record your own voice for at least one beat and compare it with the synthetic version.

Activity: build an avatar performance (65 minutes)

1. Choose text. Use a 45 to 90 second monologue or two-character excerpt. Your Lesson 2.1 scene works well. Avoid copyrighted contemporary plays; public-domain texts (Shakespeare, Chekhov, Wilde, Glaspell) or your own writing are safe.
2. Score the beats. Mark 5 to 8 beats. For each, write one word for the tactic (plead, accuse, joke, remember, decide).
3. Fill the beat sheet. For each beat, choose a gesture, an expression, a camera position, and a voice note (pace, pause). This table is your algorithm.
4. Build. In Animaker, create one scene per beat. Add the character, set the pose and expression, place the camera, and add the lines. Use the tool's AI voice for the first pass.
5. Record your own voice for at least one beat and replace the AI voice on that beat. Note the difference.
6. Sequence and transition. Order the scenes, set durations from the voice track, and add transitions only where a tactic changes sharply.
7. Evaluate. Watch it once with the sound off. Can you see the beats change? Watch again with sound. Does the voice follow the tactics? Adjust two beats.
8. Export as MP4 with captions on. Name it Lesson-2-2_LastName_Title.mp4.

Beat sheet template

Create and share

Post in the Lesson 2.2 Gallery:

• The MP4 or share link, with captions.
• Your beat sheet.
• Which beat uses your own voice, and one sentence on how it differed from the AI voice.
• A rationale of no more than 75 words: the beat that reads most clearly and why.
• Your one-line AI credit.

Reply to two peers using their beat sheet: watch with the sheet beside you and say where the performance matched the plan and where it drifted. This is co-review, the collaboration competency in practice.

Resources

• Animaker help: characters, expressions, camera, and text-to-speech.
• Synthesia or Canva video as alternatives.
• Public-domain monologue sources: Project Gutenberg, Folger Shakespeare Library.

Reflection prompts

1. What did decomposing the speech into beats reveal about the text that reading it did not?
2. Where did the AI voice flatten a tactic, and how did your own recording change it?
3. How could a student who is anxious about performing live use this activity as a bridge to the stage?

Lesson 2.2 Gallery: Share Your Avatar Performance

Share the avatar performance you built in Lesson 2.2. Start a new thread titled with the text you performed and include:

• The MP4 or share link, with captions on.
• Your beat sheet (beat, lines, tactic, gesture, expression, camera, voice).
• Which beat uses your own recorded voice, and one sentence on how it differed from the AI voice.
• A rationale of no more than 75 words: the beat that reads most clearly and why.
• Your one-line AI credit.

Reply to two peers using their beat sheet: watch with the sheet beside you and say where the performance matched the plan and where it drifted.

Lesson 2.2 Knowledge Check

Five questions on beats, variables, and AI voice in avatar performance. Unlimited attempts; your best score counts.

Lesson 2.3: Digital Choreography and Drama-Dance

Estimated time: 90 minutes. Competencies: problem solving, creativity and innovation.

Learning objectives

By the end of this lesson you will be able to:

1. Translate a dramatic idea into a movement score using the elements of dance: body, action, space, time, energy.
2. Generate short movement clips with a text-to-video tool and diagnose failures using the three-fix method.
3. Assemble a 30 to 60 second drama-dance piece that combines generated video with your own movement direction.
4. Explain how the piece would translate back to live bodies in a rehearsal room.

Warm-up: move the sentence (5 minutes)

Stand up. Take the sentence "I am not going back" and perform it three times: once with only your hands, once with only your feet, once with your whole body but no sound. Notice that each version changed the meaning. Movement is a language with its own grammar, and the five elements below are its parts of speech.

Key ideas

The elements of dance as a prompt frame

Text-to-video models respond to the same kind of specificity that music and text models do. Dance educators already have the vocabulary:

A prompt that names all five ("one dancer in an empty studio, arms leading a slow backward spiral at low level, heavy and bound, then a sudden light reach upward and freeze, 5 seconds") gives the model something to render. "A sad dance" does not.

Expect failure, then diagnose

Text-to-video is the least reliable tool in this course. Bodies drift, hands multiply, and the model may ignore your tempo. This makes it the best place to practice problem solving. For every unusable clip, name the failure and pick the fix:

1. Revise the prompt. Reduce to one dancer, one action, one quality. Add "single continuous shot" and a duration.
2. Change the tool or mode. Try an image-to-video mode with a still you make in Canva, or switch to Animaker's character animation for precise control.
3. Edit the output. Trim to the two usable seconds, slow it, mirror it, or cut on the beat. Editing turns a failed generation into a usable phrase.

Composition is yours

The model makes phrases; you make the piece. Choreographic devices such as repetition, canon, contrast, and accumulation are decisions about sequence and relationship, and they belong to you. A piece built from four generated phrases arranged with intention is a composition. The same four phrases dumped in order are a slideshow.

Activity: build a drama-dance piece (65 minutes)

1. Choose a dramatic idea. One sentence, such as "A student decides to tell the truth." Your Lesson 2.1 scene may supply one.
2. Write a movement score of four phrases. For each, write one line using all five elements. Decide on the choreographic device that will link them (repetition, canon, contrast, or accumulation).
3. Generate phrase 1 in invideo AI (or another text-to-video tool). Generate twice. Record what failed and which fix you applied.
4. Generate phrases 2 to 4. Apply what you learned from phrase 1. Keep a log of failures and fixes; the log is part of the submission.
5. Assemble. In invideo AI's editor, Canva, or CapCut, sequence the phrases using your device. Add a soundtrack (your Lesson 1.3 cue if you completed the music module, or a generated instrumental).
6. Write the transfer note. In three sentences, describe how you would teach these four phrases to live dancers or actors in a rehearsal room, and what the video showed you that improvisation would not have.
7. Export as MP4 with captions or title cards naming each phrase. Name it Lesson-2-3_LastName_Title.mp4.

Failure and fix log

Create and share

Post in the Lesson 2.3 Gallery:

• The MP4 or share link.
• Your movement score (four phrases with all five elements) and the choreographic device.
• Your failure and fix log.
• Your three-sentence transfer note.
• A rationale of no more than 75 words: the fix that taught you the most.
• Your one-line AI credit.

Reply to two peers: name the choreographic device you see in their piece, and suggest one way the piece could be performed by live bodies.

Resources

• invideo AI; Canva video and Animaker as alternatives.
• Elements of dance reference: the National Dance Education Organization standards.
• Your Lesson 1.3 cue, if available, for the soundtrack.

Reflection prompts

1. Which element of dance did the model handle worst, and how did you compensate?
2. Did generating movement change how you think about directing movement?
3. What safety, consent, or body-image conversations would this activity require in a classroom?

Lesson 2.3 Gallery: Share Your Movement Piece

Share the drama-dance piece you built in Lesson 2.3. Start a new thread titled with your piece's name and include:

• The MP4 or share link.
• Your movement score: four phrases, each using body, action, space, time, and energy, plus the choreographic device that links them.
• Your failure and fix log.
• Your three-sentence transfer note on teaching the phrases to live bodies.
• A rationale of no more than 75 words: the fix that taught you the most.
• Your one-line AI credit.

Reply to two peers: name the choreographic device you see in their piece, and suggest one way it could be performed live.

Lesson 2.3 Knowledge Check

Five questions on movement prompts, diagnosis, and composition. Unlimited attempts; your best score counts.

Lesson 2.4: Evaluating AI Feedback and Ethics in Theater

Estimated time: 75 minutes. Competencies: AI feedback evaluation, AI ethics literacy.

Learning objectives

By the end of this lesson you will be able to:

1. Elicit feedback on a dramatic text from an AI model using prompts that specify the lens and the criteria.
2. Classify each piece of AI feedback as useful, generic, or wrong, and justify the classification with reference to the text.
3. Explain the ethical questions raised by staging AI-generated or AI-assisted theater: authorship, consent, representation, labor, and disclosure.
4. Take and defend a position on whether and how you would stage AI-assisted work with students.

Warm-up: the note you ignored (5 minutes)

Recall a piece of feedback on your creative work that you rejected and were right to reject. What made you sure? Usually it is one of three things: the note misread the work, the note was so general it could apply to anything, or the note asked for a different piece than the one you were making. You will use the same three tests on AI feedback today.

Key ideas

AI feedback is fluent and unaccountable

A text model will always produce feedback, instantly and confidently, on anything you paste. It has no stake in your scene, no memory of your rehearsal room, and no way to know your students. Its feedback is a statistical average of what feedback usually looks like. That average can be genuinely useful for spotting structural problems and clichés, and it is useless or harmful when it flattens a distinctive voice toward the mean.

Three classes of AI feedback

Prompting for better feedback

Feedback quality depends on the request. Specify the lens ("read as a dramaturg preparing notes for a first rehearsal"), the criteria ("comment only on objective, obstacle, and subtext"), and the form ("five notes, each quoting the line it refers to"). Then ask a follow-up: "Which of these notes are you least confident about, and why?" Models are often more honest about uncertainty when asked directly.

Ethics of staging AI-assisted theater

Putting AI-assisted work in front of an audience raises five questions:

1. Authorship. Who is credited as playwright when a model drafted 40% of the lines? Programs and posters need honest credits.
2. Consent. Did the writers whose plays trained the model agree? Did your students agree to have their improvisations fed into a tool?
3. Representation. Models reproduce the biases of their training data. AI-generated characters from underrepresented communities can default to stereotype. Who checks?
4. Labor. Theater employs playwrights, composers, designers. Which tasks are you automating, and who would have done them?
5. Disclosure. Does the audience know? Should they? What does the school's policy say?

There is no single right answer. There is a wrong one: not asking.

Activity: evaluate the feedback, take a position (55 minutes)

1. Get feedback. Paste your Lesson 2.1 scene (or another short scene you wrote) into a text model with a lens, criteria, and form specified as above. Ask for five notes, each quoting the line it refers to.
2. Ask for uncertainty. Follow up with the confidence question. Record the answer.
3. Classify. For each of the five notes, mark it useful, generic, or wrong, and write one sentence citing the text as evidence.
4. Test one useful note. Rewrite the relevant lines. Keep both versions.
5. Take a position. In 200 to 300 words, answer: would you stage an AI-assisted scene with students? Under what conditions? Address at least three of the five ethical questions and state the credit line you would print in the program.
6. Save everything as a PDF named Lesson-2-4_LastName_Evaluation.pdf.

Create and share

Post in the Lesson 2.4 Forum:

• Your classification table with evidence.
• The before and after of the one note you tested.
• Your position statement and program credit line.
• A rationale of no more than 75 words: what surprised you most about the AI's feedback.
• Your one-line AI credit.

Reply to two peers who reached a different position. Identify the ethical question that separates you and say whether their reasoning moved you.

Resources

• Any text model.
• Your school or district's AI policy, if one exists.
• UNESCO (2024) AI competency framework for teachers, dimensions 1 and 2: human-centred mindset and ethics of AI.

Reflection prompts

1. What proportion of the feedback was useful? Would you use the tool again for this purpose?
2. Which ethical question do you feel least equipped to answer, and where would you go for help?
3. Looking back across both modules, how has your definition of "creative" changed, if at all?

Lesson 2.4 Forum: Would You Stage It?

This forum hosts the "Would you stage it?" debate for Lesson 2.4. Start a new thread titled with your position in five words or fewer and include:

• Your classification table (useful, generic, wrong) with textual evidence for each of the five AI notes.
• The before and after of the one note you tested in a rewrite.
• Your 200 to 300 word position statement and the program credit line you would print.
• A rationale of no more than 75 words: what surprised you most about the AI's feedback.
• Your one-line AI credit.

Reply to two peers who reached a different position. Name the ethical question that separates you (authorship, consent, representation, labor, or disclosure) and say whether their reasoning moved you.

Lesson 2.4 Knowledge Check

Five questions on evaluating AI feedback and the ethics of staging AI-assisted theater. Unlimited attempts; your best score counts.

Theater Module Reflection

Write a reflection of 500 to 750 words on your work across the four theater lessons. Use your Theater Module Notes: the briefs, beat sheets, movement scores, failure logs, and the reflection prompts you answered after each lesson.

Prompts to address

Address all four; use headings so the reader can find each one.

1. Growth. Choose two of the seven competencies. For each, describe one specific moment in the module where you exercised it and what you would do differently now.
2. Back to the room. Theater is live. For each of your four artifacts, write one sentence on how it would return to a rehearsal room with real students. Then pick the one with the strongest case and expand it to a paragraph.
3. Feedback and judgment. Using your Lesson 2.4 classification, describe how you will decide when to trust AI feedback on creative work, and how you will teach students to do the same.
4. Position. Restate your position on staging AI-assisted work. If you completed the music module, compare it with the classroom rule you set in Lesson 1.4. Are they consistent?

Submission

Submit a PDF or a Google Doc link. Include your name, the module, and the date at the top. Write in first person. You may use a text model to check clarity, but the reflection must be your own writing; a reflection that reads as AI-generated will be returned unmarked.

Rubric (20 points)

Designing an Arts-Integrated GenAI Lesson

You have made eight artifacts. The final project asks you to turn one of them into a lesson your future students can do. This page gives you the design frame and a template.

Design principles for arts-integrated GenAI lessons

1. Art first, tool second. The learning objective is a music or theater outcome (compose a 16-bar phrase, score a monologue into beats). The GenAI tool is the medium, and the GenAI competency is the secondary objective.
2. Make the human contribution visible. Every lesson has a step the tool cannot do: the lyric, the beat sheet, the rewrite, the failure diagnosis. Assess that step.
3. Iterate on purpose. Build in at least one "change one variable" cycle so students learn what the prompt controls.
4. Evaluate with criteria. Give students a small grid (three to six criteria) to judge outputs. Never accept "I like it."
5. Disclose and credit. Require the one-line AI credit on every artifact. Model it yourself.
6. Plan for no tool. Have a low-tech path for students without access, a blocked network, or a tool that fails on the day.
7. Ask the ethics question. Every lesson includes one moment where students consider consent, ownership, representation, or disclosure.

Lesson plan template

Copy this into a document. Fill every section.

Common mistakes to avoid

• Objectives that describe the tool ("Students will use Suno") rather than the learning.
• Rubrics that grade the AI's output quality instead of the student's decisions.
• No plan for the day the tool is down.
• Skipping the ethics moment because "there isn't time."

Before you submit

Trade drafts with a peer in the Final Project Peer Feedback forum. Use the rubric on the assignment page to give feedback. Revise before final submission.

Final Project: Arts-Integrated GenAI Lesson Plan

Design a complete lesson plan that adapts one of your eight course artifacts for a specific class you might teach. Use the template on the "Designing an Arts-Integrated GenAI Lesson" page and fill every section.

Requirements

• One lesson, 45 to 90 minutes, for a named grade band and class type.
• One arts standard and one technology or AI literacy standard.
• Two or three objectives with observable verbs, each labeled as an art outcome or a GenAI competency.
• A step-by-step activity that includes a "change one variable" cycle and an evaluation grid.
• A human-only step that the rubric assesses.
• A low-tech alternative path.
• An ethics moment.
• A rubric with three or four criteria.
• A one-page appendix: your own exemplar artifact for the lesson (this can be the course artifact you adapted) with its AI credit.

Process

1. Draft the plan.
2. Post it in the Final Project Peer Feedback forum and give feedback on two peers' drafts using the rubric below.
3. Revise. Include a short "changes made from peer feedback" note at the end.
4. Submit as a PDF or a Google Doc link.

Rubric (40 points)

Final Project Peer Feedback

Post your draft lesson plan here before final submission. Title the thread with the lesson title and grade band.

Give feedback on two peers' drafts using the four rubric criteria on the assignment page: alignment, activity design, equity and access, and ethics and disclosure. For each criterion, write one strength and one specific suggestion. Quote the section of the plan you are referring to.

When you revise, add a short note at the end of your final submission describing the changes you made from peer feedback.

Post-Course Self-Assessment

Rate yourself on the same seven competencies you rated at the start. Compare with your pre-course answers. Ungraded; one attempt.

Course Completion and Next Steps

Congratulations. You have composed, produced, scored, written, directed, and choreographed with generative AI as a collaborator, and you have built a lesson that brings one of those experiences to your future students.

Checklist for completion

When every row is checked, the instructor will issue your certificate of completion within one week. The certificate names the seven competencies and the four artifact types you produced.

Compare your self-assessments

Open your pre-course and post-course self-assessments side by side. Where did you move the most? Where did you not move? A rating that stayed the same is worth a sentence in your teaching portfolio: it tells you what to practice next.

Take it further

• Build your portfolio. Choose your three strongest artifacts and write a one-paragraph teaching statement about GenAI in the arts to accompany them.
• Run the lesson. Teach your final project lesson in a practicum or with a volunteer group. Collect student artifacts (with permission) and reflect on what changed from plan to practice.
• Join the conversation. Professional organizations for music and theater educators now host AI interest groups and conference sessions. Bring your artifacts.
• Keep reading. Long and Magerko (2020) on AI literacy; UNESCO (2024) on AI competencies for teachers; the National Core Arts Standards for the arts outcomes your lessons serve.

Tell us how it went

The post-course self-assessment includes two open questions about the course itself. Your answers shape the next offering. Thank you for your creativity, your honesty, and your care for the students you will teach.

Let's create. Let's innovate. Let's lead.