Ask an ID: Redesigning Music Essay Assignments in the Age of AI
Dear Instructional Designer,
I’m redesigning a General Education Music course. One of the major assignments is a traditional concert analysis essay where students analyze several musical works using course vocabulary. With generative AI, it feels increasingly difficult to know whether the final paper reflects the student’s own thinking. How can I redesign this assignment so it still meets the learning objectives while making student learning more visible?
-Unplugged Educator
Dear Unplugged Educator,
One of the biggest questions instructional designers are hearing right now isn’t whether students can use AI. It’s how we design assignments that still reveal what students know, notice, and understand. The good news is that the answer usually isn’t finding a better way to detect AI. It’s designing assessments that make thinking visible.
Traditional essays have long been a staple of higher education because they ask students to synthesize ideas, communicate clearly, and demonstrate understanding. The challenge today is that large language models have become remarkably good at producing polished essays. While those essays may sound convincing, they often hide the very thing instructors are trying to assess: the student’s own observations, reasoning, and growth.
That doesn’t mean essays have no place. It does mean we should ask an important question:
Is the essay the learning, or is it simply one way of communicating learning?
The AI-Responsive Assignment Design (ARAD) framework encourages us to shift our focus from protecting assignments against AI to designing assessments that make students’ thinking, decision-making, and learning process visible. Rather than relying on a single polished product, ARAD emphasizes authentic tasks, iterative thinking, reflection, and disciplinary practice.
For this General Education Music assignment, the learning objectives aren’t really about writing essays. They’re about learning to listen critically, recognize musical styles, analyze musical elements, use disciplinary vocabulary, evaluate performances, and reflect on personal growth as a listener.
With those goals in mind, here are several redesign options that preserve the learning while creating richer evidence of student thinking.
🎥 Reaction Videos
Instead of writing about music after listening, students record themselves listening in real time. They pause at key moments to explain what they notice, using musical vocabulary and timestamps to support their observations.
Students then watch classmates’ videos and extend one another’s interpretations with additional evidence from the performance.
Why I like it: This design captures authentic listening, encourages evidence-based discussion, and helps students see that musical interpretation is strengthened through dialogue rather than isolated writing. It also uses a popular format, the reaction video, that students might already be familiar with and maybe they will share it with a broader audience.
🌐 Discover This Composer Website
Students create a simple website that introduces one or more composers to a general audience. Rather than writing only for the instructor, they curate an experience for future listeners.
Pages include:
- embedded performances
- composer background
- musical observations
- listening tips
- timestamps
- course vocabulary
- connections to similar artists
Why I like it: The assignment remains analytical but becomes authentic. Students are communicating with a real audience, not simply completing a paper for a grade. This could even become a portfolio piece and be paired with one of the Micro-credential courses like Interactive Media.The website becomes a learning artifact that the student will have to demonstrate multiple skills to accomplish while also meeting the same requirements as the original essay.
💬 AI Concert Companion (Blackboard AI Conversation)
Students participate in a Blackboard AI Conversation while actively listening to assigned musical works. The AI does not analyze the music for them. Instead, it continually asks questions that prompt observation, analysis, evaluation, and reflection.
For example:
- What musical evidence supports your interpretation?
- Which musical element influenced your reaction?
- Can you identify a timestamp that illustrates your point?
- Has your interpretation changed after listening again?
Students then use that conversation to inform a final reflection or analysis.
Looking at the objectives, here’s how we could frame the discussion.
| Learning Objective | Blackboard AI Conversation Purpose |
| Identify the work | Prompt students to explain the title, composer/songwriter, artist, and style in their own words. |
| Determine musical styles | Ask students to compare stylistic characteristics and justify their conclusions. |
| Analyze musical elements | Probe for evidence using melody, rhythm, texture, timbre, harmony, dynamics, form, etc. |
| Use musical vocabulary | Encourage students to replace everyday language with disciplinary terminology. |
| Evaluate performance | Ask students to critique interpretive choices using evidence. |
| Reflect on listening | Prompt metacognitive reflection on how their listening has changed since the beginning of the course. |
Why I like it: The AI becomes a scaffold for disciplinary thinking rather than a shortcut to completing the assignment. The conversation also creates valuable evidence of the student’s learning process. I think you could add this Blackboard AI conversation into any of the other ideas and have the student start there. Or you could have this and pair it with a reflective essay,
This conversation is essentially guided formative assessment. It functions as a scaffolded inquiry that moves students through increasingly sophisticated levels of thinking.
For example, using a progression inspired by Bloom’s Taxonomy:
- Remember: What instruments or voices do you hear?
- Understand: How would you describe the musical style?
- Apply: Which musical terms best describe this section?
- Analyze: Which musical elements contribute most to the mood? How do they work together?
- Evaluate: How effective is the performance? What evidence supports your evaluation?
- Reflect/Create: How has this course changed the way you listen to music? How will you communicate these insights to your audience?
Or, even better for a music appreciation course, you could structure the AI around the disciplinary practice of close listening:
- Observe: What do you hear? (Description without judgment)
- Interpret: What might these musical choices communicate?
- Support: What evidence from the recording supports your interpretation?
- Evaluate: How effectively does the performance communicate those ideas?
- Reflect: How has your understanding changed through repeated listening?
That progression closely mirrors how music scholars and critics actually approach musical works. It also aligns naturally with the assignment’s objectives and positions the Blackboard AI Conversation as a scaffold for disciplinary thinking rather than a tool for generating a final product.
🤖 Compare Your Listening to AI’s Listening
Students first write their own observations before asking an AI tool to analyze the same musical work.
They then compare the two analyses by identifying:
- similarities
- differences
- what the AI overlooked
- which interpretation is better supported by evidence
Why I like it: Instead of outsourcing thinking, students practice evaluating AI critically. They learn that AI outputs are starting points for analysis, not authoritative answers.
🎼 Curate a Concert for a Specific Audience
Rather than selecting works simply because the assignment requires them, students curate a concert around a meaningful purpose.
Examples include:
- introducing classical music to beginners
- celebrating cultural identity
- music for resilience or healing
- dance traditions across cultures
Students justify every programming decision using evidence from the music.
Why I like it: This moves students beyond summarizing individual works toward synthesis, one of the highest levels of cognitive learning. They must think like curators rather than reporters. Also, taking something that you understand and reframing it for another audience is probably the best way to truly capture learning. If we translate the material it helps our brains realize if there are holes in the logic when we explain it to someone else.
Designing for Thinking, Not Detection
None of these redesigns eliminate writing. Instead, they broaden the ways students can demonstrate learning.
In the music department, their learning objectives also include an essay writing component so it’s easy to add a reflective essay about their learning process that can demonstrate their learning and provide that metacognitive reflection that’s so important.
Whether students are creating reaction videos, designing museum exhibits, engaging in AI-supported listening conversations, or curating concerts, the emphasis stays where it belongs: on careful observation, disciplinary reasoning, evidence, and reflection.
That’s ultimately the goal of AI-responsive assignment design. Rather than asking, “How do we stop students from using AI?” we can ask a more productive question:
“What kinds of learning experiences make student thinking impossible to hide?”
I hope this is helpful! Please don’t hesitate to reach out if you have any additional questions.
To orchestrating student success,
Your Instructional Designer
