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    AI and Machine Learning Programs for High School Students

    AI programs for high school students teach concepts, model development, or application building. Compare the technical work, prerequisites, and feedback each program offers.

    Last verified: 10 Oct 2026Reviewed by:SSSean Stevens
    10

    Programs listed

    10

    Providers represented

    3

    Online programs

    7

    In-person or hybrid programs

    Succeed compares the learning activity and entry requirements behind each program, with separate editorial judgments about student fit.

    • Scope: Succeed distinguishes AI concepts, technical model work, and application building, counting distinct programs rather than repeat sessions.

    • Official sources: Provider information supports curriculum and prerequisite statements, while fit assessments are Succeed's interpretation.

    • Last verified: This indicates when a detail was last checked, rather than a guarantee that it remains unchanged.

    • Changing details: Providers can revise curricula, fees, eligibility, and schedules between cohorts.

    • Succeed selects and compares programs independently; inclusion is not an endorsement.

    AI programs teach students how intelligent systems work, how models learn from data, or how to build applications using existing models. Technical model work involves preparing data, training models, and evaluating performance; application building may instead connect existing models to a useful interface. Prompting and AI tool use develop different skills, and a single AI workshop within a broader course does not establish a full AI program.

    Entry requirements vary with the work students undertake. Carnegie Mellon's AI Scholars requires no prior coding and provides a pre-program Python course, while Veritas AI's AI Fellowship requires Python basics or completed AI Scholars. Veritas AI Scholars expects high school mathematics, but its coding entry requirement needs clarification before enrollment. For a more demanding technical example, NYU Tandon's Machine Learning program requires precalculus and programming experience.

    Use the shortlist to compare your intended activity, preparation, teaching format, and access to feedback. UK sixth form usually refers to the final two years of secondary education; compare actual age and school-year requirements rather than assuming direct equivalence with US grades. A city, college venue, or instructor's university background does not establish who operates a program.

    Choose by the work you want to do and the preparation needed to participate.

    • Identify whether students train models, build applications, study concepts, or mainly use AI tools.

    • Match required Python skills to what you can write and debug independently.

    • Compare stated mathematics requirements with your algebra, statistics, and precalculus coursework.

    • Ask how students test performance, examine bias, and explain model limitations.

    • Distinguish individual project ownership from shared group work and instructor demonstrations.

    • Compare live teaching, mentor feedback, and independent workload.

    • Confirm school-year, nationality, language, and residential requirements before shortlisting.

    • Identify the operating provider rather than relying on a campus location or university name.

    An AI program can be worth pursuing when structured teaching and feedback help you tackle work beyond your current skills. For a typical student, a focused introduction with a manageable project can provide a useful way to test interest in the subject.

    The main limitation is depth: a short course cannot cover every mathematical and technical foundation behind modern AI. Compare the support with a smaller self-directed project, and choose a program whose workload and entry level let you participate meaningfully.

    AI programs to start comparing

    These five options show the range of providers, formats and starting levels. Compare the full list below.

    Deep Dive: AI + Finance image

    Deep Dive: AI + Finance

    Veritas AI

    Online artificial intelligence program run by Veritas AI, for ages 13-18.

    Age range: 13-18

    Cost: From $2,490

    Format: Online

    Artificial Intelligence
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    Introduction to Machine Learning image

    Introduction to Machine Learning

    Stanford Pre-Collegiate Studies

    Online artificial intelligence program run by Stanford Pre-Collegiate Studies, for ages 15-17.

    Age range: 15-17

    Cost: From $3,200

    Format: Online

    Artificial Intelligence
    Compare with other programsSave for laterView details
    Data Science and Artificial Intelligence Summer School image

    Data Science and Artificial Intelligence Summer School

    Imperial Global Summer School

    In-person artificial intelligence program run by Imperial Global Summer School in London, for ages 16-17.

    Age range: 16-17

    Cost: From £7,695

    Format: In-person

    Artificial Intelligence
    Compare with other programsSave for laterView details
    Foundations of Artificial Intelligence Summer School image

    Foundations of Artificial Intelligence Summer School

    King's College London

    In-person artificial intelligence program run by King's College London in London, for ages 18.

    Age range: 18

    Cost: From £2,550

    Format: In-person

    Artificial Intelligence
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    Data Science Academy with Python & Machine Learning image

    Data Science Academy with Python & Machine Learning

    iD Tech

    In-person artificial intelligence program run by iD Tech in New York or Cambridge or Seattle, for ages 13-18.

    Age range: 13-18

    Cost: From $4,599

    Format: In-person

    Artificial Intelligence
    Compare with other programsSave for laterView details

    Compare the highlighted programs

    CompetitionBest suited forWhy this fit
    Deep Dive: AI + Finance

    Veritas AI

    Introduction to Machine Learning

    Stanford Pre-Collegiate Studies

    Data Science and Artificial Intelligence Summer School

    Imperial Global Summer School

    Foundations of Artificial Intelligence Summer School

    King's College London

    Data Science Academy with Python & Machine Learning

    iD Tech

    How should you compare program cost and value?

    Compare the complete package and the support you will use, alongside the headline fee.

    • Carnegie Mellon covers tuition, housing, meal plans, and select program-related field trips and special events.

    • Veritas AI Scholars provides 25 class hours over 10 weeks, a group project, and a personalized mentor evaluation.

    • AI Fellowship provides 15 one-on-one mentor sessions, two publication-expert sessions, and 1–4 months of publication support.

    • Compare live teaching, independent workload, and feedback against the full fee.

    • Confirm exclusions such as travel, meals, accommodation, materials, computing resources, and optional credit processing.

    • Compare scholarship or bursary conditions and need-based aid before assuming financial support will cover your costs.

    • Judge whether the support justifies time, cost, and travel compared with a smaller self-directed project.

    Discuss this with a parent or guardian

    Agree on the learning goal, practical arrangements, and full financial commitment before paying a deposit.

    • Set a total budget that includes fees, travel, accommodation, meals, and any required technology.

    • Agree on payment timing and review deposit, cancellation, and refund terms together.

    • Confirm accommodation arrangements, supervision, and emergency contacts for residential programs.

    • Plan travel, entry documents, insurance, and arrival arrangements for international study.

    • Check live online session times against your time zone, schoolwork, and family schedule.

    • Confirm fee inclusions and the student's coding and mathematics readiness before committing.

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    Sean Stevens

    Selection reviewed by

    Sean Stevens

    Co-founder, Succeed | Founder, Immerse Education (2012–2026)

    Sean works at the intersection of academic enrichment, program quality and university preparation, with expertise in evaluating pre-university experiences for ambitious secondary school students.

    View author page

    Guides for choosing and preparing

    AI program questions from students and parents

    Some programs teach coding from the beginning. Carnegie Mellon's AI Scholars requires no prior coding and provides a preparatory Python course, while Veritas AI's AI Fellowship requires Python basics or completed AI Scholars. Confirm the entry level for the specific program you choose.

    Requirements depend on the technical depth of the course. Veritas AI Scholars expects high school mathematics, while NYU Tandon's Machine Learning program explicitly requires precalculus and programming experience. Ask which topics are prerequisites and which will be taught during the program.

    Model work involves preparing data, training or adapting a model, and testing its performance. Using AI tools focuses on applying existing systems to tasks. An application-building program may use existing models without teaching students to train their own.

    A single workshop is insufficient to establish sustained AI study. Look for a connected curriculum with repeated AI learning activities, practical work, and feedback. Assess broader computer science courses by the proportion and depth of their AI content.

    International eligibility varies by provider and program. Veritas AI describes its online programs as accessible from anywhere, while Carnegie Mellon's AI Scholars requires US citizenship or permanent residency. Compare nationality, school-year, language, and travel requirements separately.

    Group teaching can suit students seeking shared foundations and collaborative practice. Individual research mentorship can suit students ready to scope and develop their own question. Compare actual feedback, independent workload, and project ownership before choosing.

    A campus venue alone does not establish university operation or academic affiliation. Identify the named provider and any explicitly stated partnership. An instructor's university background also differs from a program being operated by that university.

    Look for a clear question, appropriate data, understandable methods, and honest evaluation. For an application, examine how outputs are tested and where the system can fail. A useful project explanation should distinguish your contribution from templates, mentor help, and existing models.

    Sources & verification

    What we verified (on 10 Oct 2026)

    • Program identities checked

    • Repeat sessions distinguished

    • Learning activities reviewed

    • Coding prerequisites compared

    • Mathematics requirements reviewed

    • Eligibility restrictions reviewed

    • Fee inclusions distinguished

    • Provider affiliations checked

    How Succeed uses this information

    We use source material to check core facts and explain why programs may be worth considering. Students and parents should still review the current official provider page before applying or paying.