1. Have two notebooks.
One notebook is for notes taken during class, and the other is for studying. Take notes as usual in the “notes” notebook, but when you get back to your dorm later, transfer those notes into the “study” notebook. Rewrite everything nicely, and organize all the information. The act of rewriting things actually helps you remember what you wrote, so just rewriting it is almost studying. When you do actually sit down and study, everything will be neat and tidy in your “study” notebook, making it easier to find information.
2. Use index cards.
Each card should have one term on the blank side, and a definition on the lined side. I have used index cards to quiz myself for years now, and it never fails. It may seem a bit elementary, but it really makes a difference when you’re trying to learn vocabulary. Physically having the cards makes it easier to study from them, rather than online cards. You can sort them into different categories to study from, and you can group them however you want. Plus, you can take them anywhere!
3. Space it out.
Learning everything all at once rarely works. When it does, it’s mostly only a short-term memory thing. If you want to actually learn the information for forever, you need to have scheduled breaks. Here’s what I do: twenty minutes of studying (learning vocab, writing it out, doing whatever needed for that class), and then a five to ten-minute break. Then, another fifteen minutes of study, and then another break. After that break, review what you studied for the past two study sections. Repeating this mini-schedule has helped me learn tons for my classes. The breaks help your brain relax so it can start working again, so do not skip the breaks.
4. Change it up.
Don’t try to study all subjects at the same time, or even on the same day, if possible. Let one day be, say, Science Day and let another be English Day. This will let you focus on just one thing at one time. You don’t want information from Math to mix in with information with Spanish!
The Illusion of Creativity
AI-generated music is often marketed as “artistic” or “educational.” But what does it really teach?
- That style can be replicated without understanding.
- That emotion can be simulated without feeling.
- That effort can be bypassed with a prompt.
For young creators, this sets a dangerous precedent. Why learn to play guitar when an algorithm can do it faster? Why study harmony when AI can generate chord progressions in seconds? The risk isn’t just laziness — it’s the loss of depth, discipline, and the tactile joy of creation.
I have listened to several AI-generated music covers. My initial reaction was one of considerable impressiveness, particularly regarding the reimagining of classic pop hits in diverse genres with novel arrangements. Here is one good example a-ha — Take On Me
However, after engaging with several such Ai generated song covers, a sense of emotional detachment emerged, attributable to the perceived absence of human artistry and genuine sentiment, resulting in a technically flawless yet ultimately soulless auditory experience.
Copyright in Crisis
What does the copyright law say about AI generated music, especially in field of music covers. Who is liable for copyright in this case, AI developers, people who used AI to generate music or people who published it on music platforms?
Recent lawsuits show that the music industry is pushing back hard:
- Universal, Sony, and Warner sued AI music platforms Suno and Udio in 2024 for allegedly training their models on copyrighted songs without permission Ars Technica The Lyon Firm.
- They’re seeking up to $150,000 per infringed song, arguing that the AI outputs resemble famous tracks and voices (e.g., Mariah Carey, Michael Jackson).
- These lawsuits target the developers of the AI tools — but they raise broader questions:The legal gray area is training data. Courts haven’t yet ruled definitively on whether using copyrighted songs to train AI counts as infringement or fair use Sites at USC.
But who’s liable?
Role | Legal Risk | Why |
AI Developers | ✅ High | For training on copyrighted material without licenses. |
Users | ⚠️ Moderate | If they intentionally recreate protected works. |
Publishers/Platforms | ✅ High | For distributing infringing content. |
The grey area lies in training data. Courts haven’t yet ruled definitively on whether using copyrighted songs to train AI counts as infringement or fair use. Meanwhile, AI-generated works themselves aren’t eligible for copyright unless a human provides substantial creative input — leaving them in legal limbo.
The Educational Mirage
Some argue that AI music helps people learn genres, production styles, or vocal techniques. But this “education” often lacks context. It’s like learning to paint by watching a printer — technically impressive, but creatively hollow.
True musical education involves:
- Struggle and iteration: Learning through mistakes.
- Emotional connection: Expressing what words can’t.
- Cultural lineage: Understanding the roots of sound.
AI shortcuts bypass all of this. And in doing so, they risk flattening the rich terrain of human creativity into a sterile landscape of algorithmic mimicry.
Why This Fight Matters
This isn’t just about music. It’s about the future of art, storytelling, and human expression. If we allow machines to dominate creative spaces, we risk turning culture into content — optimized, monetized, and devoid of soul.
We must ask: Do we want a world where art is made by machines, or one where machines help humans make better art?
5 Key Takeaways
- AI-generated music blurs the line between innovation and erosion in the creative arts
- AI-generated music raises questions about copyright, culture, education, and artistic expression
- AI-generated music can replicate style without understanding, simulate emotion without feeling, and bypass effort with a prompt
- AI-generated music sets a dangerous precedent for young creators by potentially leading to a loss of depth, discipline, and joy of creation
