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High School AI Product Planning and Testing

Grades 9-12: AI product planning, the cost of model...

Where this course comes from

The 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.

Where this course's material comes from
  • MITTool used in class
  • Stanford UniversityTool used in class
Open protected courseCheck course fit
  • Free consultation and level check
  • Apgujeong classroom or online
  • One-to-one or small group
  • Registered academy no. 11882
Three students reviewing a drawing, a laptop, pie-chart handouts and a wooden model together
Quick answer

Is High School AI Product Planning and Testing a good fit for high school students in Grades 9-12?

High School AI Product Planning and Testing is a good fit for students in grades 9-12 who are interested in AI product planning and the cost of model errors. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a data notebook with a metric memo, an App Inventor MVP with user tests, and a capstone demo with a pitch.

The course runs on MIT App Inventor and Stanford NLP tools, made by MIT and Stanford. Students start by writing down a user's problem and the current alternatives and deciding where AI actually adds value. In Jupyter notebooks with synthetic and public data they compare a rule baseline with a small classifier through a confusion matrix, test an App Inventor MVP with users, analyze Korean text with Stanza, and check bias, cost, and age-appropriate safety. The capstone ends in a go, limited pilot, iterate, or no-go decision backed by evaluation results and business analysis, presented as a demo and pitch. 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.
Parent of a CIT studentTranslated from Korean. One family's experience; the same result is not guaranteed.Read more parent feedback
Age group
Grades 9-12
Academic subject
AI & DataBrowse 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 MIT, Stanford University. Names identify the source, not affiliation or endorsement.

Where this course's material comes from
  • MITTool used in class
  • Stanford UniversityTool used in class

How can I explain this course to my child?

If questions about AI product planning or the cost of model errors keep making you ask why, High School AI Product Planning and Testing lets you investigate the question with evidence, then build and defend an extension of your own.

Which interests suggest this course?

  • AI product planning
  • the cost of model errors
  • business decisions for AI products

What does the student finish?A data notebook with a metric memo, an App Inventor MVP with user tests, and a capstone demo with a pitch.

Is this a good fit?

A strong fit for students who want to understand, build, test, or responsibly use AI systems.

Course placement follows current subject and coding readiness.

Students should be ready to document sources, methods, and limits.

Advanced tools are introduced after a clear baseline.

What will my child learn?

  1. Define the user problem, current alternatives, and success criteria, and separate what needs AI from what rules or people handle better.
  2. Compute a confusion matrix by hand and choose metrics by whether false alarms or misses cost the product more.
  3. Measure errors for subgroups and edge inputs separately and record use conditions and risks in a model card.
  4. Write an age-appropriate AI safety policy and test whether the product's actual behavior matches it.
Where this course's material comes from
  • MITTool used in class
  • Stanford UniversityTool used in class

How does the course progress?

  1. 1
    Problem Framing and Project Setup

    Define the user problem and where AI fits, run a first Jupyter notebook, and set team roles with a decision log.

  2. 2
    Data and Machine Learning Basics

    Explore a dataset in a data memo, then compare a rule baseline with a small classifier using a confusion matrix and metrics.

  3. 3
    AI Products and Korean-Language Applications

    Test an App Inventor MVP with users, analyze Korean text with Stanza, and weigh value, cost, and risk with a decision tree.

  4. 4
    Advanced Models and Responsibility

    Weigh a small neural network's gains against latency and cost, and record subgroup errors and risks in a model card.

  5. 5
    Multidimensional Evaluation and Student Safety

    Compare systems on accuracy, safety, latency, and cost with a mini-HELM benchmark and audit a student AI safety policy.

  6. 6
    Business Decisions and Capstone

    Judge opportunity, customer, value, and cost, validate the capstone with model and user tests, and pitch a launch decision.

Students documenting and explaining hands-on work at CIT

What counts as useful evidence?

A data notebook with a metric memo, an App Inventor MVP with user tests, and a capstone demo with a pitch.

What should an admissions reader be able to see?

The course runs on MIT App Inventor and Stanford NLP tools, made by MIT and Stanford. The student designs and tests features, models, and screens of their own with it. In an application or interview, the student separates the tool they used 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: 2026-09-30

Where this course's material comes from
  • MITTool used in class
  • Stanford UniversityTool used in class

Questions parents search before choosing this course

Is High School AI Product Planning and Testing a good fit for high school students in Grades 9-12?

High School AI Product Planning and Testing is a good fit for students in grades 9-12 who are interested in AI product planning and the cost of model errors. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a data notebook with a metric memo, an App Inventor MVP with user tests, and a capstone demo with a pitch.

Can my child take High School AI Product Planning and Testing 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 High School AI Product Planning and Testing?

Course placement follows current subject and coding readiness. Students should be ready to document sources, methods, and limits. Advanced tools are introduced after a clear baseline.

What will my child make or practice in High School AI Product Planning and Testing?

The main evidence is a data notebook with a metric memo, an App Inventor MVP with user tests, and a capstone demo with a pitch. Students also document decisions, tests, feedback, and limits in age-appropriate language.

How can High School AI Product Planning and Testing show research understanding in a college application?

The course runs on MIT App Inventor and Stanford NLP tools, made by MIT and Stanford. The student designs and tests features, models, and screens of their own with it. In an application or interview, the student separates the tool they used 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 High School AI Product Planning and Testing 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.
Parent of a CIT studentTranslated from Korean. One family's experience; the same result is not guaranteed.Read more parent feedback

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