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AI + Music: Data and Emotion Lab
Grades 6-8: measurable features of music, disagreement...
Where this course comes fromThe universities and labs named here made the open projects or materials this course draws on. CIT designed the course independently; it is not an official, affiliated, or endorsed course of those institutions.
- University of GenevaOpen research referenced
- Free consultation and level check
- Apgujeong classroom or online
- One-to-one or small group
- Registered academy no. 11882
Is AI + Music: Data and Emotion Lab a good fit for middle school students in Grades 6-8?
AI + Music: Data and Emotion Lab is a good fit for students in grades 6-8 who are interested in measurable features of music and disagreement between emotion labels. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a music feature table with license cards, an emotion label table with agreement rates, and an AI music prototype tested against a baseline.
The course was designed from open research published by University of Geneva. Every audio or MIDI file, homemade or permitted, gets a data card listing its creator and license before students graph its waveform and sample rate and compute BPM, energy, and pitch features. Emotion labels take two forms, categories such as bright or dark and the two axes of valence and arousal, and students measure how often different labelers agree. A simple rule serves as the baseline for small classification and regression models split by song, and the course closes with an adaptive music system the listener can override or switch off at any time. The student then designs and tests an extension of their own: a new question, feature, model, interface, or solution. In an application or interview, the student separates the source research from their own decisions, results, failed attempts, revisions, and limits.
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My child found the AI classes interesting and stayed with them. It made for a worthwhile school break. Thank you for teaching so attentively.
- Age group
- Grades 6-8
- Academic subject
- AI & Data, Arts, Media & DesignBrowse subject
- Course type
- Course
- Format
- Online or Apgujeong in person · one-to-one or small group
- Teaching language
- Korean by default, with complete English materials
- Curriculum status
- Lesson-readyEvery learning item includes the evidence, worked example, practical work, and tutor guidance required by CIT's lesson-ready standard.
From open research to student-owned work
Students inspect a relevant public source, reproduce the idea, then add an original question, feature, or test. The material this course reads was made at University of Geneva. Names identify the source, not affiliation or endorsement.
- University of GenevaOpen research referenced
How can I explain this course to my child?
If questions about measurable features of music or disagreement between emotion labels keep making you ask why, AI + Music: Data and Emotion Lab lets you investigate the question with evidence, then build and defend an extension of your own.
Which interests suggest this course?
- measurable features of music
- disagreement between emotion labels
- song-level model evaluation
What does the student finish?A music feature table with license cards, an emotion label table with agreement rates, and an AI music prototype tested against a baseline.
Is this a good fit?
A strong fit for students who want to understand, build, test, or responsibly use AI systems.
Beginners can use guided templates.
Comfort with ratios, tables, or simple graphs is helpful.
Students should be ready to revise work after feedback.
What will my child learn?
- Record the creator, license, and permitted use of every file, and inspect waveforms and sample rates as arrays and graphs.
- Compute BPM, RMS energy, and pitch summaries from homemade clips and check row counts against the raw waveform.
- Keep the emotion a piece conveys apart from the listener's own feelings, and measure labeler agreement while preserving disagreements.
- Split data by song so no song appears in both training and evaluation, and have the model decline when confidence is low or a group's errors run high.
- University of GenevaOpen research referenced
How does the course progress?
- 1Audio and MIDI Data Lineage
Make a license card for each file and inspect the waveform and sample rate of a short homemade or synthesized signal.
- 2Music Features and Emotion Measurement
Compute BPM, energy, and pitch from clips, summarize practice logs and MIDI, and compute agreement on category and valence-arousal labels.
- 3Baselines, Models, and Errors
Compare a BPM-and-energy rule with song-split classification and regression models, and set refusal thresholds from group errors.
- 4Adaptive Music Systems
Build a system that picks permitted clips from a chosen mood or simulated game state, and evaluate it on 10+ cases repeatedly.
What counts as useful evidence?
A music feature table with license cards, an emotion label table with agreement rates, and an AI music prototype tested against a baseline.
What should an admissions reader be able to see?
The course was designed from open research published by University of Geneva. The student works out what those researchers were trying to learn, why the question matters, and how they tested it, then designs and tests an extension of their own: a new question, feature, model, interface, or solution. In an application or interview, the student separates the source research from their own decisions, results, failed attempts, revisions, and limits.
Useful evidence may include a documented dataset, baseline comparison, model evaluation, error analysis, and a clear record of the student's own decisions.
These names identify who published the open project the course starts from. The evidence an application reads is what the student understood, completed, and can explain.
Which sources were reviewed?
Catalog reviewed: 2026-10-03
- University of GenevaOpen research referenced
Questions parents search before choosing this course
Is AI + Music: Data and Emotion Lab a good fit for middle school students in Grades 6-8?
AI + Music: Data and Emotion Lab is a good fit for students in grades 6-8 who are interested in measurable features of music and disagreement between emotion labels. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a music feature table with license cards, an emotion label table with agreement rates, and an AI music prototype tested against a baseline.
Can my child take AI + Music: Data and Emotion Lab online or in person, one-to-one or in a small group?
Yes. CIT offers online and in-person lessons at its Apgujeong academy in Gangnam, Seoul, with one-to-one and small-group options. A readiness consultation confirms the available format and starting point for the course.
Does my child need prior subject knowledge or coding experience for AI + Music: Data and Emotion Lab?
Beginners can use guided templates. Comfort with ratios, tables, or simple graphs is helpful. Students should be ready to revise work after feedback.
What will my child make or practice in AI + Music: Data and Emotion Lab?
The main evidence is a music feature table with license cards, an emotion label table with agreement rates, and an AI music prototype tested against a baseline. Students also document decisions, tests, feedback, and limits in age-appropriate language.
How can AI + Music: Data and Emotion Lab show research understanding in a college application?
The course was designed from open research published by University of Geneva. The student works out what those researchers were trying to learn, why the question matters, and how they tested it, then designs and tests an extension of their own: a new question, feature, model, interface, or solution. In an application or interview, the student separates the source research from their own decisions, results, failed attempts, revisions, and limits. Useful evidence may include a documented dataset, baseline comparison, model evaluation, error analysis, and a clear record of the student's own decisions.
How are the schedule and tuition for AI + Music: Data and Emotion Lab determined?
CIT confirms the student's readiness, goal, location, class size, and current availability before recommending a course plan. The consultation and level check are free; tuition is explained before enrollment.
When should a student start?
There is no fixed intake month. CIT reviews the student's current school term, readiness, and available hours, then names the point in the course where they should begin.
I appreciated that a simple early idea was extended into an 'AI debate tool'. My child has always been interested in debate and social issues, so seeing that interest connect to the project makes me expect a more complete and distinctive result.
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Choose the course after a readiness check
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