Courses & Programs

AI Native Learning Pathways for Pre-University Students

Legend College Preparatory’s Pre-University AI Native Talent Pipeline offers course packages, individual courses, labs, bridge programs, and special international pathways for students preparing to learn, build, and lead in the AI era.

Students may enter through a structured package, a summer or semester course, a robotics and simulation lab, or a research and portfolio pathway. Each offering is designed to help students produce visible evidence of learning: projects, research artifacts, datasets, dashboards, simulations, presentations, portfolios, or mission-based AI work.

This page provides an overview of current and developing offerings.

Program availability, schedule, credit status, and cohort format may vary by term, student readiness, and partner arrangement.

Featured Program Packages

Advanced Middle School Entry Package

For advanced middle school students and early ASEAN pipeline students

The Advanced Middle School Entry Package is designed for younger students who are ready to begin AI-native learning before high school.

This package introduces students to AI literacy, computational thinking, responsible AI use, beginner coding concepts, project habits, and communication skills. It is especially suitable for advanced middle school students, international bridge students, and early-stage ASEAN pipeline cohorts.

Best fit for:

  • Advanced middle school students
  • Early ASEAN pipeline students
  • Students curious about AI, coding, robotics, or data
  • Students preparing for future high school AI coursework
  • Students who need structured but age-appropriate entry into AI learning

Students may learn:

  • What AI is and how it is used
  • Responsible use of AI tools
  • Computational thinking
  • Beginner Python or logic-based problem solving
  • AI-assisted reading, writing, and research
  • Basic data awareness
  • Simple project design
  • Presentation and reflection skills
  • Early exposure to robotics or simulation

Student outputs may include:

  • Beginner AI project
  • Short research or explanation artifact
  • Simple data or visualization project
  • Presentation or demo
  • Personal learning portfolio
  • Readiness recommendation for next-level AI study

Typical next step:

High School AI Native Innovation Package, Python and AI, AI Technologies, or a summer bridge program.

High School AI Native Innovation Package

For LCP high school students and partner school students

The High School AI Native Innovation Package is designed for students ready to move beyond AI exposure and begin serious AI-native learning.

Students connect AI with academic disciplines, project-based learning, research, data, humanities, creativity, technical communication, and portfolio development. This package is appropriate for LCP students, partner school students, and international students preparing for college-level readiness.

Best fit for:

  • High school students
  • LCP students entering AI coursework
  • Partner school students
  • International students seeking structured AI learning
  • Students preparing for certification, research, robotics, or portfolio work

Students may learn:

  • AI concepts and applications
  • Python and computational thinking
  • Data analysis and visualization
  • Machine learning foundations
  • AI and humanities
  • AI ethics and human judgment
  • Project-based problem solving
  • Research and portfolio development
  • Presentation and technical communication

Student outputs may include:

  • AI course projects
  • Research summaries
  • Data dashboards
  • Prototype applications
  • AI-assisted writing or analysis
  • Capstone-style project
  • Presentation-ready portfolio artifacts

Typical next step:

AI Certification, AI Internship, Robotics and Simulation Lab, Research & Portfolio Pathway, or advanced project work.

Individual Courses

Python and AI

Build the programming foundation for AI-native learning

Python and AI introduces students to programming as a tool for reasoning, automation, data work, and AI-supported problem solving. Students learn how to write code, organize logic, work with data, and understand how programming connects to modern AI systems.

Best fit for:

  • Students new to coding
  • Students preparing for data science or machine learning
  • Students who need technical foundations
  • Students interested in automation, analysis, or AI projects

Students may learn:

  • Python syntax and programming logic
  • Variables, functions, loops, and conditionals
  • Data structures
  • Basic data handling
  • Introductory AI-related applications
  • Problem decomposition
  • Code documentation and debugging

Student outputs may include:

  • Python exercises
  • Simple AI-related scripts
  • Data mini-projects
  • Coding portfolio samples
  • Technical reflection

AI Technologies

Understand the systems shaping the AI era

AI Technologies introduces students to the major technologies behind modern artificial intelligence and their real-world applications. Students learn how AI tools, models, data systems, agents, and intelligent workflows are changing learning, work, research, creativity, and decision-making.

Best fit for:

  • Students beginning structured AI study
  • Students interested in AI applications
  • Students preparing for technical or interdisciplinary AI projects
  • Students who want to understand how AI systems are used in the real world

Students may learn:

  • Core AI concepts
  • Machine learning overview
  • Generative AI
  • AI tools and platforms
  • Data and model relationships
  • AI agents and workflows
  • Human-in-the-loop supervision
  • Responsible and ethical AI use
  • AI applications across industries

Student outputs may include:

  • AI technology brief
  • Tool comparison
  • Workflow diagram
  • Applied AI project
  • Presentation or reflection paper

Machine Learning

Learn how models make predictions and decisions

Machine Learning introduces students to the foundations of how models learn from data. Students explore classification, prediction, training data, evaluation, bias, accuracy, and responsible interpretation of model outputs.

Best fit for:

  • Students with some Python or data background
  • Students interested in AI engineering
  • Students preparing for research or technical projects
  • Students interested in model evaluation and decision systems

Students may learn:

  • Supervised learning concepts
  • Training and testing data
  • Classification and regression
  • Model evaluation
  • Accuracy, error, and bias
  • Feature selection
  • Responsible interpretation of model results
  • Introductory ML workflows

Student outputs may include:

  • Machine learning notebook
  • Model evaluation report
  • Dataset-based project
  • Visualization of results
  • Technical presentation

Data Science

Turn data into insight, explanation, and action

Data Science helps students learn how to collect, organize, analyze, visualize, and explain data. Students learn that data is not just numbers; it is evidence that must be interpreted with care, context, and human judgment.

Best fit for:

  • Students interested in research
  • Students preparing for AI, business, science, or social impact projects
  • Students who want to build dashboards or data stories
  • Students preparing for machine learning

Students may learn:

  • Data collection and cleaning
  • Tables, variables, and datasets
  • Descriptive statistics
  • Data visualization
  • Dashboard design
  • Pattern recognition
  • Responsible data interpretation
  • Communicating findings

Student outputs may include:

  • Dataset analysis
  • Visualization portfolio
  • Dashboard
  • Data story
  • Research-supported presentation

AI and Humanities

Study AI through language, history, ethics, culture, and human meaning

AI and Humanities helps students examine how artificial intelligence changes the way people write, read, remember, create, interpret, and make decisions. Students study AI not only as a technology, but as a force shaping culture, knowledge, ethics, and human responsibility.

Best fit for:

  • Students interested in writing, history, ethics, literature, society, or philosophy
  • Students who want to use AI responsibly in academic work
  • Students preparing for interdisciplinary research
  • Students who need stronger human reasoning alongside AI tool use

Students may learn:

  • AI-assisted reading and writing
  • Historical interpretation in the AI era
  • AI and creativity
  • AI ethics
  • Bias and representation
  • Human judgment and responsibility
  • Source evaluation
  • Argumentation and reflection

Student outputs may include:

  • AI-assisted research paper
  • Ethical analysis
  • Historical or cultural interpretation
  • Reflection portfolio
  • Presentation on AI and society

AI Internship

Apply AI learning to real projects and professional-style work

AI Internship is designed for students ready to apply AI knowledge in a more independent, project-based, or mentor-guided setting. Students may work on research artifacts, prototypes, data projects, AI workflows, educational tools, or partner-supported challenges.

Best fit for:

  • Students who have completed prior AI coursework
  • Students ready for independent or team-based projects
  • Students preparing for college applications or advanced portfolios
  • Students who can work responsibly with mentor feedback

Students may work on:

  • Applied AI projects
  • Research summaries
  • Data dashboards
  • AI workflow design
  • Educational AI tools
  • Robotics or simulation support
  • Portfolio documentation
  • Presentation and final reflection

Student outputs may include:

  • Internship project artifact
  • Project documentation
  • Mentor or teacher feedback
  • Final presentation
  • Portfolio-ready work sample

Labs and Applied Programs

Robotics and Simulation Lab

Bring AI from screen to physical systems

The Robotics and Simulation Lab is designed for students ready to apply AI to robotics, simulation, sensors, mission planning, and real-world decision workflows.

Students learn through simulation-first design, robotics experimentation, data collection, route planning, scenario modeling, and human-supervised AI workflows. This lab may support themes such as smart campus operations, emergency response, urban flooding, environmental monitoring, or autonomous systems.

Best fit for:

  • Students interested in robotics
  • Students ready for hands-on and simulation-based learning
  • Students preparing for engineering, AI, or physical AI pathways
  • Students who want to connect coding, data, hardware, and decision-making

Students may learn:

  • Robotics fundamentals
  • Simulation-first design
  • Sensors and data logs
  • Route planning
  • Mission workflow design
  • Human-in-the-loop decision-making
  • AI-assisted reporting
  • System testing and evaluation

Student outputs may include:

  • Robotics demo
  • Simulation video
  • Mission workflow diagram
  • Technical log
  • Scenario report
  • Team presentation
  • Portfolio-ready robotics artifact

Research & Portfolio Development

Build evidence for college and future opportunities

Research & Portfolio Development helps students organize their best work into visible, explainable evidence of learning. Students may develop research artifacts, project documentation, presentations, GitHub repositories, dashboards, videos, or capstone-style portfolios.

Best fit for:

  • Students preparing for college applications
  • Students completing AI projects
  • Students seeking internships, showcases, or competitions
  • Students who need help turning work into strong portfolio evidence

Students may develop:

  • Research summaries
  • AI project pages
  • Code documentation
  • Data visualizations
  • Capstone presentations
  • Reflection essays
  • Demo videos
  • Portfolio websites or folders

Student outputs may include:

  • Structured digital portfolio
  • Project narrative
  • Presentation-ready artifact
  • Research or technical documentation
  • Final reflection

Summer, Bridge, and International Programs

Summer AI Programs

Focused AI learning during summer

Summer AI Programs provide students with a structured opportunity to begin or accelerate AI learning during the summer term. These programs may include individual AI courses, certification pathways, project labs, research support, or bridge preparation for the next academic year.

Best fit for:

  • Students who want focused summer learning
  • Students preparing for high school or college-level AI work
  • International students seeking short-term AI exposure
  • Students preparing for certification or portfolio development

Possible formats:

  • Short intensive course
  • Multi-week summer course
  • Project-based summer cohort
  • AI certification sequence
  • Research and portfolio workshop
  • Robotics or simulation bootcamp

Student outputs may include:

  • Completed course modules
  • Summer AI project
  • Certificate or completion record, where applicable
  • Portfolio artifact
  • Final presentation

Bridge Programs

Prepare students for the next level of AI-native learning

Bridge Programs help students transition into more advanced AI coursework or LCP-related pathways. These programs may support students entering from middle school, partner schools, international schools, or ASEAN cohorts.

Best fit for:

  • Students entering LCP AI programs
  • Students from partner schools
  • International students
  • Students needing academic, technical, or language preparation
  • Students preparing for high school AI coursework

Students may work on:

  • Academic readiness
  • English reasoning and technical communication
  • AI literacy
  • Computational thinking
  • Beginner coding
  • Portfolio habits
  • Project presentation skills

Student outputs may include:

  • Readiness assessment
  • Introductory project
  • Learning portfolio
  • Placement recommendation
  • Bridge completion summary

Taiwan Special Programs

Asia-facing AI learning connected to LCP’s academic ecosystem

Taiwan Special Programs connect LCP’s AI Native Talent Pipeline with Taiwan’s strengths in AI, engineering, robotics, semiconductor industries, education, and innovation.

These programs may include Taiwan-based cohorts, partner school programs, teacher collaboration, student showcases, AI course delivery, and bridge programs connected to LCP’s U.S. academic structure.

Best fit for:

  • Taiwan students and families
  • Taiwan partner schools
  • Students seeking U.S.-connected AI learning
  • Students preparing for bilingual or international pathways
  • Taiwan-based educators and partners

Possible program areas:

  • AI course cohorts
  • Teacher-supported AI learning
  • Student project showcases
  • AI and humanities
  • Robotics and simulation
  • Portfolio development
  • Taiwan-to-U.S. bridge programs
  • Taiwan-to-ASEAN collaboration

Student outputs may include:

  • AI projects
  • Research or portfolio artifacts
  • Presentation materials
  • Certificate or completion record, where applicable
  • Pathway recommendation for future LCP programs

ASEAN Special Programs

Building regional AI-native talent pipelines

ASEAN Special Programs are designed for partner schools, organizations, and student cohorts in Malaysia, Indonesia, and other ASEAN contexts.

These programs support early AI talent development through structured courses, bridge programs, certification pathways, robotics and simulation exposure, and portfolio-based learning. The goal is to help students develop AI-native capacity before college while connecting local talent needs with global academic and industry contexts.

Best fit for:

  • ASEAN partner schools
  • Malaysia and Indonesia student cohorts
  • Advanced middle school and high school students
  • Students preparing for AI, engineering, robotics, or data pathways
  • Regional partners building future AI talent pipelines

Possible program areas:

  • Advanced Middle School Entry Package
  • High School AI Native Innovation Package
  • AI certification cohorts
  • Robotics and simulation exposure
  • Data and machine learning projects
  • Student showcase preparation
  • Teacher collaboration
  • Silicon Valley / Taiwan / ASEAN bridge activities

Student outputs may include:

  • Course projects
  • AI portfolio artifacts
  • Simulation or robotics demos
  • Research summaries
  • Data dashboards
  • Presentations
  • Certificate or completion record, where applicable

Suggested Course Progressions

For Younger Students

Advanced Middle School Entry Package → Python and AI → AI Technologies → High School AI Native Innovation Package

This pathway helps younger students build confidence, reasoning, coding foundations, and project habits before moving into higher-level AI work.

For High School Beginners

AI Technologies → Python and AI → Data Science → AI and Humanities or Machine Learning

This pathway gives students a balanced foundation in AI concepts, coding, data, human judgment, and applied project work.

For Technical Students

Python and AI → Data Science → Machine Learning → Robotics and Simulation Lab → AI Internship

This pathway is suitable for students interested in engineering, AI systems, robotics, data, and technical project production.

For Interdisciplinary Students

AI and Humanities → AI Technologies → Data Science → Research & Portfolio Development → AI Internship

This pathway is suitable for students interested in writing, society, ethics, research, communication, policy, business, or interdisciplinary innovation.

For Portfolio-Focused Students

AI Certification → Research & Portfolio Development → AI Internship → Student Showcase

This pathway is suitable for students preparing for college applications, internships, competitions, showcases, or advanced opportunities.

How to Choose the Right Program

Students and families should consider:

  • Current grade level
  • Math readiness
  • Coding experience
  • English and communication readiness
  • Interest in AI, robotics, data, humanities, or research
  • Ability to work independently
  • Prior project experience
  • College and career goals
  • Available schedule
  • Desired outcome: course credit, certificate, portfolio, project, or exploration

LCP can help students identify the most appropriate starting point.

Program Outcomes

Across the AI Native Talent Pipeline, students may produce:

  • AI projects
  • Research artifacts
  • Coding notebooks
  • Data dashboards
  • Machine learning reports
  • Robotics or simulation demos
  • AI workflow diagrams
  • Ethical reflection papers
  • Technical presentations
  • Digital portfolios
  • Capstone projects
  • Internship-style project documentation

The goal is for students to leave with more than exposure. They should leave with evidence of what they can understand, build, explain, and improve.

For Students and Families

Students and families interested in AI Native courses and programs may contact Legend College Preparatory to discuss placement, readiness, course availability, summer options, and pathway planning.

For Schools and Partners

Schools, education organizations, universities, companies, and regional partners interested in offering AI Native programs may contact LCP to discuss partnership models, cohort design, teacher collaboration, course packages, and implementation timelines.

LCP welcomes partners who want to help prepare pre-university students to become thoughtful builders, responsible innovators, and future leaders in the AI era.