Teaching in the Age of AI: How Music Educators Can Stay Ahead of the Curve

Antonella Di Giulio

MTNA Business Digest, Volume 5, Issue 2

January 2026

We're living through a pivotal moment in music education. Artificial intelligence isn't science fiction anymore—it's in our classrooms, studios, and practice rooms. Tools can generate sheet music, offer instant feedback, create practice plans, and even compose in the style of Chopin or Taylor Swift. This technology is fundamentally changing how music is taught, learned, and imagined.

I should be transparent: I wrote this article with AI assistance during the drafting and editorial process: outlining, language refinement, structural editing. But I take full responsibility for the content, interpretation, and conclusions. The tools I used are ChatGPT, Claude, and Midjourney.

I started using AI tools a while back to speed up my workflows and refine my thinking. It became something like having a mirror that could challenge and clarify my ideas. Through this experimentation, I've come to believe that the more productive question isn't what AI will do to us; it's how we can use these tools to enhance what we already do best and remain essential in a rapidly evolving landscape.

Reconceptualizing Our Relationship to Technology

Rather than chasing the latest AI platforms—which will change before this article is even published—what matters more is the mindset shift AI invites. It's less about mastering trendy apps and more about recognizing we now have the ability to ask better questions, find solutions faster, and build creative applications we never imagined possible. Even without knowing how to code.

For music educators, this means three things:

1. Asking: How can I solve this problem more efficiently?
2. Exploring: Is there a faster or more creative way to scaffold this learning objective?
3. Creating: Could I prototype something tailored to my studio—even if I've never written a line of code?

This shift empowers us to move from being users to becoming architects and strategists, from reacting to technology to actively shaping how it serves our students and our values. Rapidly changing technology encourages experimentation, iteration, and taking ownership of the learning environment in genuinely new ways.

From Standardization to Adaptive Design

One of the most powerful things AI enables is the shift from generalized instruction to highly personalized, adaptive learning design. We've always known that differentiated instruction matters; the research has acknowledged the limitations of one-size-fits-all curricula for years (Tomlinson, 2001; Subban, 2006). But practical constraints have historically limited our ability to respond to each student's unique learning path: not just skill level, but motivation, identity, pace, even emotional state.

AI tools offer a pathway toward more granular personalization. But this doesn't happen automatically. It has to be designed intentionally and thoughtfully by dedicated teachers.

A Framework for Adaptive Pathway Design

Here's a protocol I've developed through continuous refinement in my own studio. I created a Custom GPT tailored specifically for my teaching style and studio needs. This lets me generate consistent prompts that reflect my philosophy, student profiles, and repertoire preferences.

How to Start:

• Visit chat.openai.com and click "Explore GPTs."
• Select "Create a GPT" and walk through the setup.
• Input your teaching approach, examples of students you serve, preferred warm-up styles, vocabulary you use.
• Give specific instructions on tone, behavior, necessary scaffolding.
• Upload documents as needed.

Once created, you can call on your assistant anytime: "Generate a lesson plan for a 9-year-old violinist preparing for their first recital," or "Create a motivational message for a teen pianist feeling stuck on arpeggios."

Phase 1: Profile the Student.

The first step is building a clear snapshot of your student's personality, goals, and learning patterns. I've found ChatGPT remarkably helpful here. You might ask it: "Create a learner profile for a 13-year-old piano student who prefers improvising to reading, loves movie soundtracks, struggles with hand coordination, and gets discouraged when they make mistakes."

What you get back is a thoughtful summary of strengths, challenges, and motivational strategies. It's like having a colleague help you think through what makes this particular student tick. Then save that chat in ChatGPT's "Projects" with each student's name. I have one chat for each of my students.

Phase 2: Generate Adaptive Content.

Once you have that profile, you can turn it into actual materials in that same chat. This is where things get practical fast. You might ask ChatGPT to "Write a 5-minute warm-up for left-hand dexterity using finger patterns based on the D minor scale," or "Suggest three short piano pieces with rich harmonies and simple rhythms for a student who loves cinematic music."

If you're working on theory, try, "Create a music theory worksheet on intervals that looks like a puzzle game." The system generates exercises, repertoire suggestions, worksheets, practice games—all instantly customizable to what you know about this learner.

Phase 3: Build a Weekly Flow.

With content in hand, you can structure teaching around that student's evolving needs. I might prompt: "Create a weekly practice routine for this student: 20 minutes of repertoire, 10 minutes of technique, 10 minutes of improvisation tied to their piece."

What comes back is a coherent practice flow with pacing guidance and integrated goals: not just a list of activities, but a thoughtful structure that accounts for how this student learns best.

Phase 4: Reflect, Adjust, Empower.

Here's where it gets interesting. You can use AI not just to teach, but to help you understand how your student learns. I encourage students to engage with my custom GPT directly. They might ask, "Why do I keep missing this rhythm?" or "Can you quiz me on the key signatures I keep forgetting?"

This kind of on-demand support between lessons can be transformative for student agency. You can also use tools like Canva Documents integrated with ChatGPT to create reflection journals or design badges for milestones. The goal is making learning visible and giving students ownership over their progress.

Iterate and Refine.

Once this system is in place, you can duplicate and tweak it for each student. Gradually you're building a studio where every learner follows a path that reflects who they are, not just what they're learning.

Beyond the Studio

Individualization doesn't stop with lesson planning. It shapes how we run our studios entirely.

AI-Powered Support:

• Teaching: Custom GPTs for lesson plans, adaptive exercises
• Business Management: Automated scheduling, invoices, emails, reminders
• Social Media: Auto-generated captions, campaigns, video scripts
• Content Creation: Downloadable lead magnets, quizzes, PDFs, email sequences
• Strategy: Model pricing tiers, test engagement ideas, analyze retention

In this evolving landscape, the most successful educators won't be doing everything manually. They'll be the ones who design systems that scale human impact without burning out. And yet, this raises questions about labor, expertise, and professional identity. As routine tasks become automated, what remains distinctively human in teaching?

I'd argue it's the dimensions that resist automation: interpretive judgment, emotional attunement, aesthetic discernment. In other words, it’s the capacity to recognize and nurture what's emergent and unpredictable in each student's development.

Critical Considerations

We need to acknowledge real concerns. Data privacy is significant, especially with minors. When we use AI tools to generate student profiles or track progress, we're creating data trails. Who owns that information? How is it being used beyond our immediate purposes? What happens if a platform changes its terms of service or experiences a breach? We need to address these questions proactively with students and families.

Then there's algorithmic bias. AI systems are trained on existing data, which means they perpetuate and amplify existing biases in music education. If the training data predominantly reflects Western classical traditions, the tool may struggle to support culturally responsive teaching in other musical traditions. If it's been trained primarily on data from economically privileged contexts, it might generate recommendations that assume resource access many students don't have. We need to remain critically aware of these limitations and actively counter them in our practice (Holmes et al., 2019; Selwyn, 2019).

We need to be honest about what we don't know. We lack longitudinal research on AI-mediated instruction in music education specifically. What are the long-term effects on student musicianship? On motivation and persistence? On the teacher-student relationship? On students' development of autonomous learning skills? We simply don't have answers yet. That demands ongoing critical reflection and a commitment to adjusting course as we learn more.

Music learning is fundamentally relational and embodied. No algorithm can substitute for the knowledge that flows through physical demonstration, the interpretive dialogue of coaching, or the affective attunement between teacher and student. The most irreplaceable aspects of our work—recognizing a student's emotional state and adjusting accordingly, modeling artistic interpretation through live performance, offering encouragement that comes from genuine relationship—these can't be automated. We need to be vigilant that our use of AI tools supports rather than supplants these essential human dimensions of teaching.

In Closing

AI will continue to evolve. But amid all the change, one truth remains: The soul of music education lies in human connection, creativity, and care. We're not being asked to compete with machines; we're being invited to reimagine what it means to teach, lead, and inspire in a rapidly changing world.

This is a time to design boldly and commit to the human heart of our work. The future belongs to those who stay rooted in meaning while reaching toward possibility.

AI Toolbox

References

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.

Subban, P. (2006). Differentiated instruction: A research basis. International Education Journal, 7(7), 935-947.

Tomlinson, C. A. (2001). How to differentiate instruction in mixed-ability classrooms (2nd ed.). Association for Supervision and Curriculum Development.

 

Antonella Di Giulio is a pianist with a PhD in Music Theory. She founded Musica IQ and Woom Talent Center, where she promotes an interdisciplinary approach to music instruction.


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