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Build real AI projects with real mentors — for students in grades 7–12
Motivated students work with graduate students and experienced practitioners on focused AI projects with real deliverables — apps, research papers, and presentations that strengthen college applications and build the confidence to keep doing meaningful work.
Summer 2026 Enrollment Open
Build real AI projects with real mentors — for students in grades 7–12
Motivated students work with graduate students and experienced practitioners on focused AI projects with real deliverables — apps, research papers, and presentations that strengthen college applications and build the confidence to keep doing meaningful work.
30+
Years Combined Experience
Yale · Stanford · Brown
Academic Foundations
AI Researchers + K–12 Educators
Built for How Students Learn
What Students Have Built
Real students. Real projects. Real outcomes. These artifacts and achievements define what's possible at the Academy.
Building AI tools during SeqHub AI Academy helped me stand out in the hiring process and helped me land my role at Amora Health.
Isabella
Demo days were incredibly helpful.
I got to share my progress, receive feedback, and get inspired by other projects.
Forrest
SeqHub AI Academy allows students to conduct real, high-impact research while motivating curiosity and independent inquiry.
Marina
PROOF, NOT PROMISES
What Students Have Built
Real students. Real projects. Real outcomes. These artifacts and achievements define what's possible at the Academy.
Academy to Career
Kiersten
Summer 2022 & 2023 - Project Lab + TA
Chinese Language Tutor
Built an AI-powered chatbot for conversational language learning. Turned it into her high school senior project, working with Chinese and CS teachers to deploy it.
WHAT HAPPENED NEXT
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Project became resume centerpiece for interviews
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Sparked passion for educational technology
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Now sophomore at Vanderbilt University studying CS and Math
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Actively contributing to AI research
Built an AI-powered chatbot for conversational language learning. Turned it into her high school senior project, working with Chinese and CS teachers to deploy it.
Conference Publication
Marina
Summer 2025 - 1-on-1 Mentorship
Medical AI Trustworthiness Research
Evaluated whether improving LLM accuracy in medical question-answering introduces demographic bias. Systematically altered patient gender and ethnicity across healthcare datasets while keeping correct answers constant.
WHAT HAPPENED NEXT
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Co-authored paper accepted to NeurIPS workshop
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Presented findings at conference in California
Evaluated whether improving LLM accuracy in medical question-answering introduces demographic bias. Systematically altered patient gender and ethnicity across healthcare datasets while keeping correct answers constant.
Micro-Cohort + 1-on-1 → Career
Isabella
Summer 2023 & 2024 - Project Lab → 1-on-1 Mentorship
Take Five — AI Mental Health Companion
Built a web app helping teenagers implement 5 minutes of daily self-care. Enhanced it with a Gemini-powered chatbot that recommends activities based on user preferences and history.
WHAT HAPPENED NEXT
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Submitted to Google Gemini API Developer Competition
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Secured role as Youth Research Lead at Emora Health
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Admitted to Georgetown University
Built a web app helping teenagers implement 5 minutes of daily self-care. Enhanced it with a Gemini-powered chatbot that recommends activities based on user preferences and history.
Research + Social Impact
Hugh
Summer 2024 - Project Lab
Women's Rights Data Analysis
Partnered with an international women's rights nonprofit to analyze 10 years of data from 20+ countries. Applied AI techniques from the Academy to transform large text datasets into digestible visualizations revealing actionable insights.
WHAT HAPPENED NEXT
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Produced publishable insights from 10 years of data across 20+ countries
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Currently co-authoring article for wider publication
Partnered with an international women's rights nonprofit to analyze 10 years of data from 20+ countries. Applied AI techniques from the Academy to transform large text datasets into digestible visualizations revealing actionable insights.
Publication + Conference
Hongyu
Summer 2024 & 2025 - Discovery → 1-on-1 Mentorship
AI Bias in Historical Chinese Translation
Used LLMs to research his great-grandfather, a Qing dynasty official turned revolutionary. Investigated how AI handles ancient Chinese text and discovered systematic bias patterns. Currently researching AI sycophancy in historical language processing.
WHAT HAPPENED NEXT
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Produced publishable insights from 10 years of data across 20+ countries
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Currently co-authoring article for wider publication
Used LLMs to research his great-grandfather, a Qing dynasty official turned revolutionary. Investigated how AI handles ancient Chinese text and discovered systematic bias patterns. Currently researching AI sycophancy in historical language processing.
PROOF, NOT PROMISES
Discovery Program Projects
First-time builders in our 3-week introductory program
CardVault
by Evan
Trading card platform with PSA grading guide and cross-account bidding for sports cards
Bond
by Nisa
A dating app for chemical elements to make learning chemistry fun
Escape Room
by Dominic
Interactive puzzle game with AI-generated hints
AI Theater Director
by Alessandro
Helps theater students analyze and practice scripts
Peaceful Mind
by Inioluwa
Meditation app with guided body scans, journaling, and an AI companion for stress relief
What Students Actually Do
Build and Deploy AI-Powered Applications
Students create functional apps—financial analysis tools, mental health platforms, educational systems—used in competitions, college applications, and real-world contexts.
Co-Author Research Accepted to Conferences
Students contribute to peer-reviewed research. Past work has been accepted to venues like NeurIPS and presented at academic conferences.
Present Work at Demo Days and Competitions
Every program culminates in presentations. Students pitch to mentors, peers, and external audiences—building communication skills alongside technical depth.
Continue Projects Beyond the Program
Many students extend their work independently or through Academic Year mentorship—publishing research, entering competitions, or contributing to ongoing systems.
This isn't a lecture series or a coding bootcamp. Students produce real work with real outcomes.
Summer 2026 Programs
Intensive, project-driven, mentor-led. Three pathways designed for students ready to do real work—with demo days, deliverables, and clear continuation paths.
ENTRY POINT
Discovery Program
Open to students in grades 7-12
3 weeks
Small cohort (≤10 students)
Multiple sessions available between June 15 – August 1
Exact dates confirmed upon enrollment
Build your first AI project and develop the judgment to use AI tools effectively. Students develop critical evaluation skills and leave with a clear sense of readiness for advanced programs.
Best for: first-time learners with no prior experience, or returning students who want to build something new in a guided, exploratory environment. Students who thrive here often continue to Project Lab.
HOW IT WORKS
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Live sessions with your cohort and mentor each week
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Weekly demo days where students present work and receive peer and mentor feedback
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Guided async work between sessions
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Clear milestones and regular feedback
WHAT STUDENTS DO
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Use real AI tools (ChatGPT, Claude, Lovable, NotebookLM) for different purposes — research, coding, content creation
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Learn to question AI output: spot errors, validate claims, understand limitations
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Learn with AI through our AI Co-teacher, which guides without giving answers
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Design and build an AI-powered application based on their own interests
WHAT THEY PRODUCE
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A working AI-powered app they're excited to show (games, tools, creative projects)
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Experience using AI as a collaborator, not a crutch
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Foundation for advanced programs (Project Lab, 1:1 Mentorship)
Pathway to: Advanced Micro-Cohorts
FLAGSHIP PROGRAM
Project Lab
Grades 9-12
5 weeks
4 students per team (by design)
Multiple project teams running between June 15 – August 7
Each team focuses on a distinct project with a dedicated mentor
Work on real, scoped projects — research studies, working applications, or tools built for real users. Each team operates like a small consulting group—collaborative, accountable, and focused on producing artifacts that matter: apps, research contributions, technical reports. Culminates in demo day presentations.
Team Size: Four students allows for real collaboration without diluting individual ownership. Each student is accountable for meaningful contributions, while mentors maintain high standards and direction.
Best for: students who want to work on a real project in a small team — whether they've completed Discovery or have prior experience with AI tools and building. Strong contributors may be invited to 1:1 Mentorship.
HOW IT WORKS
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Weekly mentor sessions focused on direction and feedback
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Weekly demo days where students present work and receive peer and mentor feedback
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Team collaboration on shared deliverables
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Structured checkpoints and iteration cycles
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Final presentation of project artifacts
Example Project Areas
Projects are inspired by real research, industry problems, or community needs — scoped to be completed in five weeks with a demo-ready artifact. Students contribute to work that has led to conference presentations, publications, and working applications.
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Replicate published research on LLM hiring bias — test whether "fairness prompts" actually work when models evaluate real resumes
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Analyze how demographic signals (names, affiliations) influence AI decisions in hiring, healthcare, and education
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Build bias audit tools that measure model behavior across race, gender, and cultural contexts
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Test educational AI systems for vulnerabilities — prompt injection, trojanized inputs, adversarial attacks
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Replicate Anthropic's research on agentic misalignment — when do AI systems pursue goals that conflict with user intent?
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Design safety guards for AI automation pipelines (calendar, email, document systems)
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Build pipelines to analyze executive communications — extract topics, sentiment, and speech patterns from audio
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Develop frameworks for audio-to-text-to-insight analysis applicable to brand monitoring, compliance, or research
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Evaluate LLM performance on real-world health datasets
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Contribute to medical AI validation with international research partners
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Contribute to LTD (Look-Think-Do) — our AI co-teacher platform used in real classrooms
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vBuild RAG systems that retrieve and synthesize information for specific domains
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WHAT STUDENTS DO
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Tackle authentic problems from research or industry contexts
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Collaborate in a small team with clear roles and deliverables
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Receive direct, regular feedback from your expert mentor
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Navigate open-ended challenges with structured support
WHAT THEY PRODUCE
Project artifact ready to share — application, research contribution, or working tool
Technical report, prototype, or deployed product
Confidence working on open-ended problems
Exposure to real research and applied workflows
MENTORSHIP MODEL
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Primary mentor (graduate student or experienced practitioner)
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Co-learner / TA for debugging and momentum
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AI co-teacher for async scaffolding
WHY THIS MATTERS
Students leave with work they can point to: apps submitted to competitions, research contributions, technical reports. Past students have used this work for conference presentations, college applications, and roles at tech companies.
Pathway to: Advanced Micro-Cohorts
DEEPEST DIVE
1-on-1 Mentorship
Grades 10-12
5 weeks
Individual, fully customized
5 weeks within the June 15 – August 8 summer window
Exact schedule determined with your mentor
For students ready to pursue depth or specialization. Work independently on a mentor-guided project tailored to your interests and goals. This is the top of the ladder—not the default path—offered to students who have demonstrated exceptional readiness and commitment.
WHAT STUDENTS DO
-
Tackle authentic problems from research or industry contexts
-
Collaborate in a small team with clear roles and deliverables
-
Receive direct, regular feedback from your expert mentor
-
Navigate open-ended challenges with structured support
WHAT THEY PRODUCE
-
Project artifact ready to share — application, research contribution, or working tool
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Technical report, prototype, or deployed product
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Confidence working on open-ended problems
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Exposure to real research and applied workflows
Academic Year Mentorship Program
For students ready to continue meaningful work. Flexible, remote, and mentor-guided—enabling publications, competitions, and real contributions across semesters.
The Academic Year Program is a continuation pathway, not a separate product. Students who demonstrate readiness during summer programs—or through direct application—work with mentors throughout the school year on projects with real stakes.
Longer timelines enable deeper outcomes. Multi-semester projects create space for conference submissions, peer-reviewed publications, competition entries, and sustained contributions to ongoing research or systems.
High expectations. Serious work. Flexible structure. These are complementary, not contradictory—enabling deeper independent work while maintaining meaningful mentor relationships.
See where it leads
Kiersten started as a student, became a teaching assistant, and is now pursuing AI research at Vanderbilt. Her SeqHub project became her high school senior project — and a centerpiece of her resume.
Research Track
For students pursuing publications, conference presentations, or contributions to academic research.
Applied Track
For students building applications, entering competitions, or contributing to real-world systems.
Continuation Track
For students extending summer work with ongoing mentor guidance and structured support.
Flexible, Remote Structure
Weekly or bi-weekly sessions guided by mentor and student goals. Work at your own pace without compromising depth.
Sustained Mentor Relationships
Continue with graduate students and experienced practitioners who know your work, your trajectory, and your potential.
Publications & Competitions
Multi-semester projects create space for conference submissions, competition entries, and peer-reviewed contributions.
Independence Over Time
Develop the confidence and skills to continue meaningful work—with mentor support available when you need it.
Mentorship, Not Instruction
Our mentors don't lecture. They guide, challenge, and collaborate—moving real work forward.
Mentors are researchers, practitioners, and builders with demonstrated expertise in their field. Every mentor brings real-world context, academic rigor, and the ability to guide students through open-ended challenges.
Mentorship is collaborative and personalized. Students don't watch videos or complete exercises. They work on authentic problems with structured support, regular feedback, and high expectations.
The focus is on moving real work forward. Mentors help students navigate complexity, make decisions under uncertainty, and produce artifacts that matter—papers, apps, research contributions.
Primary Mentor
Graduate student or experienced practitioner
Direction, judgment, feedback
Teaching Assistant
Graduate or advanced scholar
Supporting mentors and students, debugging, momentum
AI Co-Teacher
Async scaffolding system
Available when mentors aren't
Why Families Trust This
Families care about results—and we show ours.
Evidence-driven: We show outcomes, not promises
Selective enrollment: Quality over quantity
Real mentors: PhD-level researchers and practitioners, not teaching assistants
Tangible artifacts: Students leave with work they can point to
Not pay-to-play: Admission is based on readiness, not payment
This is not tutoring. This is not a camp. This is not passive learning. This is identity-forming, mentor-guided, outcome-proven work.
Why SeqHub AI Academy
We're AI researchers and K–12 educators with 30+ years of combined experience and advanced degrees from Yale, Stanford, and Brown. We build AI applications. We evaluate AI systems. We know what they can do — and where they break. And we know how students learn.
Most programs teach students to use AI. We go further: motivated students collaborate on real projects with experienced practitioners and researchers — learning to question AI's outputs, identify its limitations, and apply it responsibly.
Students leave with more than skills. They leave with judgment, agency, and the confidence to keep building with AI on their own.
Responsible AI Use & Parent Visibility
AI is powerful, and students need guidance. All programs emphasize responsible use, critical evaluation, and judgment—not blind dependence.
Students work within a structured learning environment supported by mentors, teaching assistants, and an AI co-teacher designed for learning—not shortcuts.
Our goal is not just skill acquisition, but thoughtful, healthy engagement with emerging technology.
How Enrollment Works
Enrollment is selective to protect quality and mentor capacity.
1
Submit a short application
Tell us about your student's background, interests, and goals.
2
We assess readiness and fit
We review each application to match students with the right program and cohort.
3
Families receive confirmation
Accepted students receive next steps, cohort details, and preparation materials.
Mentor-Guided Foundations
A five-week intensive experience designed for beginners to build strong foundations with direct mentorship.
Be paired with an industry mentor and choose between working on an existing SeqHub project or designing your own.
Live, small-group instruction to build something fun (a virtual pet or educational tutor)
Lessons in prompt engineering, AI tool strengths & risks
Post-workshop reflection + 1-month sandbox access so you can keep experimenting
Academic Year-Long Mentor-Guided Experience
Our most selective program, the Year-Long Mentor-Guided Experience supports up to 10 exceptional students per year in developing advanced skills in AI research and applications. Designed for high school and undergraduate students ready to take on sustained projects, this program blends rigorous research with practical development.
What You'll Explore
Students choose from three primary tracks, each designed to balance foundational knowledge, research skills, and real-world applications:
Foundations of Programming
Project-based mastery of Java and Python for motivated beginners.
AI & Social Science Research
Explore AI’s inner workings, ethics, alignment, bias, and societal impact.
AI Applications Development
Contribute to real SeqHub platforms such as our AI Co-Teacher.
Program Options
Academic Year Focus
Program
20 weeks, Sept–May
$9,375
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25 hours of 1:1 mentorship
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3–4 hours per week commitment
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Deliverables: Research paper or technical contributions to a deployed system
Comprehensive Year-Round Program
30 weeks, Sept–Aug
$11,725
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35 hours of 1:1 mentorship, plus a summer intensive
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3–4 hours per week during school year; up to 8 hours weekly in summer
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Deliverables: Research paper or substantial technical contributions
Using AI to Build AI
A course for learners who want to build web apps aided by AI. Over ~7-10 hours/week, you’ll learn front-end fundamentals (HTML, CSS, JavaScript), prompt engineering, and how to integrate AI via APIs. Projects include mini-apps (chatbots, adventure stories, content generators), culminating in a capstone web application powered by LLMs.
Critical AI Literacy
Designed for entry-level professionals or anyone who interacts with AI tools in work or daily life and wants to understand more than just how to use AI. Through the Look → Think → Do framework, this course explores how AI works, where it fails (bias, hallucinations, etc.), how to write better prompts, and how to build simple AI tools or workflows without needing to code. Includes live labs, demo days, and weekly artifacts.
Java Mastery Course
A project-based deep dive into Java for those who want more technical foundation. With ~7-10 hours/week, live sessions, and guided project work, students reinforce Java fundamentals (primitive types, control structures, recursion, OOP), engage with data structures & algorithms, and build personalized projects aligned with their interests. Great for learners who want to strengthen problem-solving and prepare for things like the AP Java exam or college CS coursework.