Teaching

Teaching Portfolio

An AI-first curriculum built for the future. Students learn how AI works, how to prompt it, and how to evaluate and use it responsibly — through principles, design thinking, hands-on labs, and portfolio projects that prepare them for an AI-driven world.

Every course follows a flipped classroom model grounded in Bloom's taxonomy: lectures and exam-style practice questions are completed at home, while class time is spent on daily hands-on labs, AI tooling, and collaboration — with no terminal exam.

As a CS/IT teacher, I automate homework and lab grading — I don't mark by hand — so my time and energy go to teaching, collaboration, and developing students' skills.

Secondary Computer Science & IT Teacher · Advocate for AI & Digital Learning

Luka (Yee) Thao

Vientiane, Laos

I am a secondary computer science and information technology (CS/IT) teacher with an EAL (English as an Additional Language) lens. My route into the classroom runs through 15+ years as a systems engineer — a deliberate transition, not a fallback. That background is exactly why I can teach computer science and digital literacy as one connected practice, and why I bring working fluency with the AI tools schools are only now beginning to reckon with.

I teach a full computer science and information technology program — AP Computer Science Principles, AP Computer Science A, AP Cybersecurity, IB DP Computer Science, and IB DP Digital Society — the modern, AI-assisted way: analysis- and debugging-first, and grounded in real industry practice, so students keep their exam and university-credit credentials while leaving genuinely prepared for the AI economy.

Teaching & MentorshipLiteracy tutoring, EAL support, and classroom experience

Teaching Assistant — Mid-State Technical College (Stevens Point, WI): my most recent educator role, supporting instruction in AI and computer science.
Homeroom summer school teacher — Jane Addams School for Democracy (Jun – Aug 2011): supervised a summer homeroom, guiding students through a rotating weekly schedule of classes and activities.
Volunteer teacher — Jane Addams School for Democracy (2006–2011): taught English, math, and science to Hmong children and adults in nightly classes using a Reciprocal Peer Teaching model, where the student is sometimes the teacher.
EAL student mentor & communication coach — scaffolding complex language for non-native learners.
Former executive officer, Toastmasters — public-speaking and communication leadership.
America Reads award — recognised for childhood literacy tutoring; the work was featured in national news coverage.
Keynote speaker — addressed thousands of school students at the University of Minnesota's Get Ready college-prep program, and introduced Dr. Harry Boyte, a national figure in civic engagement, at a university event.

The transition: engineer → educator

I began in literacy tutoring, spent 15+ years in software engineering, and am now returning to education full-time. Those engineering years were never a detour from teaching — they gave me the systems thinking, the AI fluency, and the curriculum-design discipline I now bring to the classroom. Teaching is the destination, not the fallback.

Industry & technical backgroundThe experience behind the teaching

Digital Transformation Solutions — architected GovSpace within the Cambridge SupTech Lab network (partnered with Cambridge University).
ADMAIS — founder & CTO; building AI-powered learning tools for underserved communities.
Skinship AI — co-founder & CTO; built an AI-powered, mobile-first skincare community app (2023–2026).
Earlier ventures — founder of Let Them Pharm (2022) and co-founder of We R' United (2018).
Renaissance Learning — enterprise learning-analytics and student-data privacy (EdTech).
Earlier roles — 3M, Jostens, Airvuz, Western National Insurance and Businessolver (2011–2023).

Credentials

120 Hour TESOL/TEFL certified.
Schrole Educator Profile — complete, with referees listed.
Designer of the vertically-aligned curriculum on the Courses and Syllabus tabs.

Credit-bearing · university-credit exams

3 College Board AP · 2 International Baccalaureate — university-credit exams

AP Computer Science Principles (AI-Focused)College Board APFull academic year

Frameworks: College Board AP CSP (2027–28 AI redesign) · data, networks & internet safety · AI Design Project · language-neutral pseudocode

Prerequisite: None

Assessment: Understanding-first, AI allowed · Create Task + AI Design Project · portfolio

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

Module 1: Computational Thinking & Algorithms

Abstraction, decomposition, pattern recognition, and algorithmic thinking — conceptually, not raw syntax.
Daily in-class labs: use AI to explain and evaluate an algorithm, then spot where it goes wrong.

Artifact: An Algorithm Analysis Notebook

Before class: Watch the computational-thinking lecture; answer exam-style practice questions.

In class: Hands-on lab: analyze and debug an AI-explained algorithm.

Module 2: Data & Information

How data represents information; analyzing and visualizing data to draw conclusions.
Daily in-class labs: use AI-assisted tools to analyze a dataset and verify its claims.

Artifact: A Data Analysis & Findings Report

Before class: Watch the data lecture; answer exam-style practice questions.

In class: Hands-on lab: analyze a dataset with AI-assisted tools and verify.

Module 3: AI & Machine Learning (AI Design Project)

How AI and ML work, and how to design an AI solution for a real problem.
Daily in-class labs: build an AI Design Project with no-code tools and prompt engineering.

Artifact: An AI Design Project

Before class: Watch the AI & ML lecture; answer exam-style practice questions.

In class: Hands-on lab: design and prototype an AI solution.

Module 4: Networks, Internet Safety & Impact

How the internet and networks work, plus privacy, bias, and the societal impact of computing.
Daily in-class labs: audit AI output for bias and prepare an internet-safety and impact case study.

Artifact: An Internet Safety & Impact Case Study

Before class: Watch the networks & impact lecture; answer exam-style practice questions.

In class: Hands-on lab: audit AI for bias and write an internet-safety case study.

AP Computer Science A (Java)College Board APFull academic year

Frameworks: College Board AP Computer Science A · Java · object-oriented programming · algorithmic logic

Prerequisite: Algebra I & basic logic familiarity

Assessment: 50% multiple-choice (40 questions) · 50% free-response (4 code questions) · portfolio + labs

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

Module 1: Java Fundamentals & Control Flow

Primitive types, objects, boolean logic, and iteration — the core of Java syntax and control flow.
Daily in-class labs: write, debug, and trace Java with AI pair-programming.

Artifact: A foundational Java program

Before class: Watch the Java fundamentals lectures; answer exam-style practice questions.

In class: Hands-on lab: write and debug Java with AI pair-programming.

Module 2: Classes & Object-Oriented Design

Writing classes, encapsulation, and inheritance — object-oriented architecture and code reuse.
Daily in-class labs: refactor code into clean classes and class hierarchies.

Artifact: An object-oriented class hierarchy

Before class: Watch the classes & inheritance lectures; answer exam-style practice questions.

In class: Hands-on lab: refactor code into classes and hierarchies.

Module 3: Data Structures — Arrays & Collections

1D arrays, ArrayLists, and 2D arrays — managing and traversing collections of data.
Daily in-class labs: implement and audit array/collection algorithms.

Artifact: A data-structures program

Before class: Watch the arrays & collections lectures; answer exam-style practice questions.

In class: Hands-on lab: implement and audit array/collection algorithms.

Module 4: Algorithms, Recursion & Exam Prep

Recursion, searching/sorting, and AP free-response practice.
Daily in-class labs: trace recursive methods and solve free-response questions.

Artifact: A recursive algorithm + exam practice set

Before class: Watch the recursion & algorithms lectures; answer exam-style practice questions.

In class: Hands-on lab: trace recursion and solve free-response questions.

AP Cybersecurity (AI-Assisted)College Board APFull academic year

Frameworks: College Board AP Cybersecurity · first exam May 2027 · data encryption · network architecture · cyber defense · AI-assisted

Prerequisite: None

Assessment: 100% digital AP exam (first sitting May 2027) · zero active coding · AI-assisted labs

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

Module 1: Introduction to Security

Social engineering, types of cyber adversaries, and the impact of AI in cybersecurity.
Daily in-class labs: use AI to research a social-engineering attack and analyze how AI is used both offensively and defensively.

Artifact: An Adversary & AI Impact Brief

Before class: Watch the intro-to-security lecture; answer exam-style practice questions.

In class: Hands-on lab: analyze a social-engineering attack with AI assistance.

Module 2: Securing Physical Spaces

Physical vulnerabilities and attacks, protecting physical spaces, and detecting physical breaches.
Daily in-class labs: use AI to audit a physical security layout and identify gaps.

Artifact: A Physical Security Audit

Before class: Watch the physical-security lecture; answer exam-style practice questions.

In class: Hands-on lab: audit a physical security layout with AI-assisted tools.

Module 3: Securing Networks

Network segmentation, firewalls, wireless security, and detecting attacks from log files.
Daily in-class labs: use AI-assisted tools to analyze network logs and spot indicators of compromise (IoCs).

Artifact: A Network Log Analysis

Before class: Watch the network-security lecture; answer exam-style practice questions.

In class: Hands-on lab: analyze network logs and detect IoCs with AI-assisted tools.

Module 4: Securing Devices

Device vulnerabilities, authentication, anti-malware, and detecting attacks on devices.
Daily in-class labs: use AI to analyze device logs and identify indicators of compromise.

Artifact: A Device Security Audit

Before class: Watch the device-security lecture; answer exam-style practice questions.

In class: Hands-on lab: analyze device logs and detect attacks with AI-assisted tools.

Module 5: Securing Applications & Data

Access controls, cryptography, and protecting data in storage and transit — plus detecting application attacks.
Daily in-class labs: use AI-assisted tools to identify evidence of common application attacks in logs.

Artifact: An Application & Data Security Audit

Before class: Watch the application & data security lecture; answer exam-style practice questions.

In class: Hands-on lab: detect application and data attacks with AI-assisted tools.

IB DP Computer Science (New Syllabus)IB Diploma ProgrammeTwo-year sequence

Frameworks: IB DP Computer Science (new syllabus) · 2-year sequence · hardware, networks, databases & Machine Learning · text code analysis (Java or Python) · IA project

Prerequisite: None

Assessment: Understanding-first, AI allowed · IA software project (open internet + documentation) · exams on systemic concepts, not syntax

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

Module 1: System Fundamentals & Hardware Architecture

Theme A1 — Computer Fundamentals: CPU architecture, system components, binary/Gray code, and logic gates.
Daily in-class labs: map CPU execution paths and analyze hardware trade-offs with AI design assistants.

Artifact: A System Analysis & Impact Report

Before class: Watch the architecture & binary logic lectures; complete algorithmic translation practice.

In class: Hands-on lab: map CPU execution paths and audit hardware trade-offs with AI.

Module 2: Introduction to Programming & Text Code Analysis

Theme B1–B2 — Computational thinking and programming elements: sequential logic, variables, conditionals, loops, and 1D arrays.
Daily in-class labs: debug logic errors and build multi-layered array architectures with AI pair-programmers.

Artifact: Core Algorithmic Proficiency Exam (conceptual; zero syntax penalties)

Before class: Watch the text-code trace lectures; complete manual trace tables.

In class: Hands-on lab: debug logic with AI pair-programmers and build array architectures.

Module 3: Computer Networks & Digital Infrastructure

Theme A2 — Computer Networks: architecture, topologies, data transmission, packet switching, and cloud infrastructure.
Daily in-class labs: model a secure cloud subnet and audit protocol vulnerabilities with AI simulation tools.

Artifact: A Network Design & Security Case Study

Before class: Watch the network models and IP addressing lectures; solve packet-routing problems.

In class: Hands-on lab: model a secure cloud subnet and audit protocol vulnerabilities with AI.

Module 4: Relational Databases & Schema Design

Theme A3 — Databases: relational fundamentals, normalization, schemas, and ERDs.
Daily in-class labs: normalize a schema and run AI-assisted SQL queries against multi-table data.

Artifact: Database Design Blueprint & SQL Query Set

Before class: Watch the data-integrity and normalization lectures; build logical ERDs.

In class: Hands-on lab: normalize a schema and test SQL queries with AI assistance.

Module 5: Machine Learning & Intelligent Systems

Theme A4 — Machine Learning (mandatory): supervised, unsupervised, reinforcement and deep learning, plus socio-ethical impacts.
Daily in-class labs: train, cross-validate, and audit an ML model for training-data skew.

Artifact: A Machine Learning Model & Bias Evaluation Project

Before class: Watch the neural-network and training-data lectures; solve classification scenarios.

In class: Hands-on lab: train and audit an ML model for bias with AI frameworks.

Module 6: Object-Oriented Programming (OOP)

Theme B2–B3 — Advanced programming: classes, instantiation, encapsulation, and inheritance.
Daily in-class labs: refactor linear code into modular OOP structures with AI blueprint validation.

Artifact: OOP Modular Architecture Map

Before class: Watch the class-design lectures; sketch object interaction maps.

In class: Hands-on lab: refactor a codebase into OOP structures with AI validation.

Module 7: The Internal Assessment (IA) Computational Solution

The mandatory SL/HL IA: scope, prototype, and document a computational solution.
Daily in-class labs: build the production codebase with open-internet documentation, using AI as an architectural consultant.

Artifact: The Final IA Software Project (documentation + functionality video)

Before class: Record system-component updates; write user-criteria documentation.

In class: Hands-on lab: scope, prototype, and build the IA using AI as an architectural consultant.

Module 8: IB Pre-Released Case Study & Examination Prep

Official pre-released case study dossier (Paper 1, Part B) and multi-theme review.
Daily in-class labs: simulate Paper 1 (Theme A + case study) and Paper 2 (Theme B coding analysis).

Artifact: Mock Paper 1 & Paper 2 (understanding-first)

Before class: Deconstruct the case-study dossier; review system vocabulary.

In class: Hands-on lab: simulate Paper 1 and Paper 2 under understanding-first conditions.

IB DP Digital Society (SL/HL)IB Diploma ProgrammeTwo-year sequence

Frameworks: IB DP Digital Society (SL/HL) · no-code · interdisciplinary (Group 3) · data, algorithms, networks, robots, AI & global challenges

Prerequisite: None

Assessment: Inquiry Project (IA) — analytical report + multimedia presentation · zero programming required

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

Module 1: Data, Information & Systemic Change

Core Concepts: Data, Information & Digital Systems — expression, identity, and security interventions.
Daily in-class labs: research a localized data-access issue and evaluate its societal equity with AI analytics.

Artifact: A Data & Society Case Study

Before class: Watch the data-governance lectures; complete exam-style practice on privacy frameworks.

In class: Hands-on lab: research a data-access issue and evaluate its equity with AI.

Module 2: Algorithms, Automation & AI Foundations

Core Concepts: Algorithms & AI ecosystems — decision-making systems and automated workflows.
Daily in-class labs: audit an automated profiling or sorting mechanism for demographic bias.

Artifact: An Algorithm & AI Ethics Analysis Report

Before class: Watch the algorithm & automated-bias lectures; resolve exam-style transparency matrices.

In class: Hands-on lab: audit an automated profiling system for bias with AI-assisted mockups.

Module 3: Digital Networks, Infrastructure & Globalization

Core Concepts: Networked Societies & Digital Space — infrastructure disparity, space, time, and globalized access.
Daily in-class labs: map the socioeconomic impact of physical versus satellite infrastructure rollouts.

Artifact: Global Infrastructure Continuity Report

Before class: Watch the digital-divide and network-topology lectures; answer scenario questions.

In class: Hands-on lab: map infrastructure impact in developing regions with AI geospatial tools.

Module 4: Robots, Extended Realities & Emerging Tech (HL Integrated)

Concepts in Context: Robots, AI & Emerging Realities — automation substitution and synthetic realities.
Daily in-class labs: track an automation transition and map downstream societal dependencies and risks.

Artifact: A Robotics & Automation Socioeconomic Impact Matrix

Before class: Watch the automation and deep-learning lectures; analyze labor-displacement case studies.

In class: Hands-on lab: map an industry automation transition with AI mapping tools.

Module 5: Global Challenges & Policy Interventions

Global Challenges: sustainability, well-being, governance, human rights, and geopolitics.
Daily in-class labs: roleplay an international tech-regulation panel and pressure-test legal frameworks.

Artifact: Legislative Policy Briefing Document

Before class: Watch the tech-waste and cross-border surveillance lectures; draft mock regulatory responses.

In class: Hands-on lab: roleplay a regulation panel and pressure-test frameworks with AI.

Module 6: The Internal Assessment (IA) Digital Society Inquiry

The mandatory IA portfolio: a real-world digital-impacts inquiry with an analytical report.
Daily in-class labs: formulate, refine, and edit the inquiry portfolio and multimedia presentation.

Artifact: Final Inquiry Portfolio (report + multimedia presentation)

Before class: Record presentation voiceovers; compile source-verification matrices.

In class: Hands-on lab: refine the inquiry portfolio with AI-assisted formatting and citations.

Foundational & intermediate electives

Introduction to Computational Logic & AI Prompt EngineeringSemester

Frameworks: Foundational elective · computational logic without syntax pressure · cloud architecture · prompt engineering for research · digital literacy

Prerequisite: None

Assessment: Understanding-first, AI allowed · portfolio + project · no terminal exam

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

Module 1: Logic Foundations — Variables & Loops

Variables, data types, and the core logic of loops and conditionals — the entry-level architecture of computation.
Daily in-class labs: use AI to explain and trace a simple program, then predict its output.

Artifact: A Logic Trace Journal

Before class: Watch the logic-foundations lecture; answer exam-style practice questions.

In class: Hands-on lab: trace and predict program output with AI assistance.

Module 2: Functions & Cloud Architecture

Breaking problems into functions, inputs/outputs, and reusable blocks of logic — plus a first look at cloud architecture (servers, storage, services).
Daily in-class labs: use AI to refactor a sequence into functions and map how it would run on cloud infrastructure.

Artifact: A Refactored Logic & Cloud Map

Before class: Watch the functions & cloud lecture; answer exam-style practice questions.

In class: Hands-on lab: refactor a program and map its cloud architecture with AI assistance.

Module 3: Prompt Engineering for Academic Research

Writing precise prompts, iterating, and evaluating AI output for research and study.
Daily in-class labs: build a prompt library for research, note-taking, and study workflows.

Artifact: A Research Prompt Toolkit

Before class: Watch the prompt-engineering lecture; answer exam-style practice questions.

In class: Hands-on lab: build and test a research prompt library.

Module 4: Digital Literacy & Responsible AI

Evaluating sources, spotting misinformation, privacy, and responsible AI use.
Daily in-class labs: fact-check AI output and audit a source for bias.

Artifact: A Digital Literacy Field Guide

Before class: Watch the digital-literacy lecture; answer exam-style practice questions.

In class: Hands-on lab: fact-check AI output and audit a source for bias.

AI Ethics, Law, & Digital CitizenshipSemester

Frameworks: Humanities/tech hybrid · zero code · AI copyright, data privacy, algorithm bias, surveillance & automation

Prerequisite: None

Assessment: Understanding-first, discussion & debate · portfolio · no programming

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

Module 1: Data Privacy & Algorithm Bias

How personal data is collected and used, and how algorithms can encode bias.
Daily in-class labs: analyze a real algorithm for bias and debate its data-privacy tradeoffs.

Artifact: A Privacy & Bias Case Analysis

Before class: Watch the privacy & bias lecture; answer exam-style practice questions.

In class: Hands-on lab: analyze an algorithm for bias and debate its privacy tradeoffs.

Module 2: AI Copyright & Intellectual Property

Who owns AI-generated work, and how copyright law is adapting to generative AI.
Daily in-class labs: debate an AI copyright case and draft a position.

Artifact: A Copyright Position Paper

Before class: Watch the AI copyright lecture; answer exam-style practice questions.

In class: Hands-on lab: debate an AI copyright case and draft a position.

Module 3: Surveillance, Facial Recognition & Civil Liberties

How facial recognition and surveillance technology affect privacy and civil liberties.
Daily in-class labs: evaluate a surveillance scenario and weigh security against rights.

Artifact: A Surveillance & Rights Evaluation

Before class: Watch the surveillance lecture; answer exam-style practice questions.

In class: Hands-on lab: evaluate a surveillance scenario and weigh security against rights.

Module 4: Automation & the Future of Work

The social impact of automation on jobs, economies, and communities.
Daily in-class labs: research an automation case study and map its human impact.

Artifact: An Automation Impact Report

Before class: Watch the automation & work lecture; answer exam-style practice questions.

In class: Hands-on lab: research an automation case and map its human impact.

Java Programming & UX/UI DesignSemester

Frameworks: Intermediate elective · AI-assisted Java (generate, debug & audit) · UX/UI design · bridge to AP Computer Science A

Prerequisite: Introduction to Computational Logic & AI Prompt Engineering

Assessment: Understanding-first, AI allowed · project-based · portfolio + demo

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

Module 1: Java Foundations with AI Assistants

Core Java logic — variables, control flow, and functions — built with AI coding assistants as a pair programmer.
Daily in-class labs: use an AI assistant to scaffold and debug a small Java program.

Artifact: A Java Foundations Notebook

Before class: Watch the Java-foundations lecture; answer exam-style practice questions.

In class: Hands-on lab: scaffold and debug a Java program with an AI assistant.

Module 2: Data Structures & Program Design

Arrays, collections, and file handling — structuring programs that scale.
Daily in-class labs: refactor a program with AI-assisted analysis and testing.

Artifact: A Structured Java App

Before class: Watch the data-structures lecture; answer exam-style practice questions.

In class: Hands-on lab: refactor and test a Java app with AI assistance.

Module 3: UX/UI Prototyping & System Workflows

Industry-standard UX/UI prototyping: wireframes, user flows, and system maps.
Daily in-class labs: use AI-driven design tools to prototype an interface and map a workflow.

Artifact: A UX Prototype & User Flow Map

Before class: Watch the UX/UI lecture; answer exam-style practice questions.

In class: Hands-on lab: prototype an interface and map a workflow with AI-driven design tools.

Module 4: Product Design & User Evaluation

Evaluating user experience, iterating on design, and presenting a finished product.
Daily in-class labs: run a usability test and use AI to synthesize feedback.

Artifact: A Product Demo & UX Evaluation

Before class: Watch the product-design lecture; answer exam-style practice questions.

In class: Hands-on lab: run a usability test and synthesize feedback with AI.

Data Visualization & AI AnalyticsSemester

Frameworks: Business/tech hybrid · spreadsheets & dashboards · AI analytics · zero hard programming

Prerequisite: None

Assessment: Understanding-first, AI allowed · portfolio of dashboards · no database code

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

Module 1: Data Storytelling & Chart Design

Turning raw data into clean charts, graphs, and infographics that tell a story.
Daily in-class labs: build charts from a real dataset and refine their clarity.

Artifact: A Chart & Infographic Set

Before class: Watch the data-storytelling lecture; answer exam-style practice questions.

In class: Hands-on lab: build charts from a real dataset and refine their clarity.

Module 2: Spreadsheets & Dashboards

Using spreadsheets and visual dashboards to explore and summarize data.
Daily in-class labs: build an interactive dashboard from a dataset.

Artifact: An Interactive Dashboard

Before class: Watch the dashboards lecture; answer exam-style practice questions.

In class: Hands-on lab: build an interactive dashboard from a dataset.

Module 3: AI Analytics & Pattern Discovery

Using AI analytics engines to find patterns in data — without writing database code.
Daily in-class labs: use an AI analytics tool to surface insights and verify them.

Artifact: An AI Insights Report

Before class: Watch the AI analytics lecture; answer exam-style practice questions.

In class: Hands-on lab: use an AI analytics tool to surface and verify insights.

Module 4: The Data Storytelling Project

A capstone: turn a large dataset into a clean, persuasive data story with visuals.
Daily in-class labs: build and present a complete data story.

Artifact: A Data Story Presentation

Before class: Watch the capstone lecture; answer exam-style practice questions.

In class: Hands-on lab: build and present a complete data story.

Note: These module structures are a flexible guideline, not a final schedule. The actual daily and weekly breakdown will be adapted to your school's academic calendar, term length, and examination schedule.

AP Computer Science Principles (AI-Focused)Full academic year · Prerequisite: None

Frameworks: College Board AP CSP (2027–28 AI redesign) · data, networks & internet safety · AI Design Project · language-neutral pseudocode

Assessment: Understanding-first, AI allowed · Create Task + AI Design Project · portfolio

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

View full syllabus ↗

Module 1: Computational Thinking & Algorithms — Weeks 1–6 · An Algorithm Analysis NotebookBefore: Watch the computational-thinking lecture; answer exam-style practice questions. · In class: Hands-on lab: analyze and debug an AI-explained algorithm.
Module 2: Data & Information — Weeks 7–12 · A Data Analysis & Findings ReportBefore: Watch the data lecture; answer exam-style practice questions. · In class: Hands-on lab: analyze a dataset with AI-assisted tools and verify.
Module 3: AI & Machine Learning (AI Design Project) — Weeks 13–18 · An AI Design ProjectBefore: Watch the AI & ML lecture; answer exam-style practice questions. · In class: Hands-on lab: design and prototype an AI solution.
Module 4: Networks, Internet Safety & Impact — Weeks 19–24 · An Internet Safety & Impact Case StudyBefore: Watch the networks & impact lecture; answer exam-style practice questions. · In class: Hands-on lab: audit AI for bias and write an internet-safety case study.

AP Computer Science A (Java)Full academic year · Prerequisite: Algebra I & basic logic familiarity

Frameworks: College Board AP Computer Science A · Java · object-oriented programming · algorithmic logic

Assessment: 50% multiple-choice (40 questions) · 50% free-response (4 code questions) · portfolio + labs

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

View full syllabus ↗

Module 1: Java Fundamentals & Control Flow — Units 1–4 · A foundational Java programBefore: Watch the Java fundamentals lectures; answer exam-style practice questions. · In class: Hands-on lab: write and debug Java with AI pair-programming.
Module 2: Classes & Object-Oriented Design — Units 5 & 9 · An object-oriented class hierarchyBefore: Watch the classes & inheritance lectures; answer exam-style practice questions. · In class: Hands-on lab: refactor code into classes and hierarchies.
Module 3: Data Structures — Arrays & Collections — Units 6–8 · A data-structures programBefore: Watch the arrays & collections lectures; answer exam-style practice questions. · In class: Hands-on lab: implement and audit array/collection algorithms.
Module 4: Algorithms, Recursion & Exam Prep — Unit 10 · A recursive algorithm + exam practice setBefore: Watch the recursion & algorithms lectures; answer exam-style practice questions. · In class: Hands-on lab: trace recursion and solve free-response questions.

AP Cybersecurity (AI-Assisted)Full academic year · Prerequisite: None

Frameworks: College Board AP Cybersecurity · first exam May 2027 · data encryption · network architecture · cyber defense · AI-assisted

Assessment: 100% digital AP exam (first sitting May 2027) · zero active coding · AI-assisted labs

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

View full syllabus ↗

Module 1: Introduction to Security — Weeks 1–5 · An Adversary & AI Impact BriefBefore: Watch the intro-to-security lecture; answer exam-style practice questions. · In class: Hands-on lab: analyze a social-engineering attack with AI assistance.
Module 2: Securing Physical Spaces — Weeks 6–9 · A Physical Security AuditBefore: Watch the physical-security lecture; answer exam-style practice questions. · In class: Hands-on lab: audit a physical security layout with AI-assisted tools.
Module 3: Securing Networks — Weeks 10–14 · A Network Log AnalysisBefore: Watch the network-security lecture; answer exam-style practice questions. · In class: Hands-on lab: analyze network logs and detect IoCs with AI-assisted tools.
Module 4: Securing Devices — Weeks 15–18 · A Device Security AuditBefore: Watch the device-security lecture; answer exam-style practice questions. · In class: Hands-on lab: analyze device logs and detect attacks with AI-assisted tools.
Module 5: Securing Applications & Data — Weeks 19–24 · An Application & Data Security AuditBefore: Watch the application & data security lecture; answer exam-style practice questions. · In class: Hands-on lab: detect application and data attacks with AI-assisted tools.

IB DP Computer Science (New Syllabus)Two-year sequence · Prerequisite: None

Frameworks: IB DP Computer Science (new syllabus) · 2-year sequence · hardware, networks, databases & Machine Learning · text code analysis (Java or Python) · IA project

Assessment: Understanding-first, AI allowed · IA software project (open internet + documentation) · exams on systemic concepts, not syntax

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

View full syllabus ↗

Module 1: System Fundamentals & Hardware Architecture — 15% of course hours · A System Analysis & Impact ReportBefore: Watch the architecture & binary logic lectures; complete algorithmic translation practice. · In class: Hands-on lab: map CPU execution paths and audit hardware trade-offs with AI.
Module 2: Introduction to Programming & Text Code Analysis — 20% of course hours · Core Algorithmic Proficiency Exam (conceptual; zero syntax penalties)Before: Watch the text-code trace lectures; complete manual trace tables. · In class: Hands-on lab: debug logic with AI pair-programmers and build array architectures.
Module 3: Computer Networks & Digital Infrastructure — 15% of course hours · A Network Design & Security Case StudyBefore: Watch the network models and IP addressing lectures; solve packet-routing problems. · In class: Hands-on lab: model a secure cloud subnet and audit protocol vulnerabilities with AI.
Module 4: Relational Databases & Schema Design — 10% of course hours · Database Design Blueprint & SQL Query SetBefore: Watch the data-integrity and normalization lectures; build logical ERDs. · In class: Hands-on lab: normalize a schema and test SQL queries with AI assistance.
Module 5: Machine Learning & Intelligent Systems — 15% of course hours · A Machine Learning Model & Bias Evaluation ProjectBefore: Watch the neural-network and training-data lectures; solve classification scenarios. · In class: Hands-on lab: train and audit an ML model for bias with AI frameworks.
Module 6: Object-Oriented Programming (OOP) — 10% of course hours · OOP Modular Architecture MapBefore: Watch the class-design lectures; sketch object interaction maps. · In class: Hands-on lab: refactor a codebase into OOP structures with AI validation.
Module 7: The Internal Assessment (IA) Computational Solution — 15% of course hours · The Final IA Software Project (documentation + functionality video)Before: Record system-component updates; write user-criteria documentation. · In class: Hands-on lab: scope, prototype, and build the IA using AI as an architectural consultant.
Module 8: IB Pre-Released Case Study & Examination Prep — Flexible integration · Mock Paper 1 & Paper 2 (understanding-first)Before: Deconstruct the case-study dossier; review system vocabulary. · In class: Hands-on lab: simulate Paper 1 and Paper 2 under understanding-first conditions.

IB DP Digital Society (SL/HL)Two-year sequence · Prerequisite: None

Frameworks: IB DP Digital Society (SL/HL) · no-code · interdisciplinary (Group 3) · data, algorithms, networks, robots, AI & global challenges

Assessment: Inquiry Project (IA) — analytical report + multimedia presentation · zero programming required

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

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Module 1: Data, Information & Systemic Change — 20% of course hours · A Data & Society Case StudyBefore: Watch the data-governance lectures; complete exam-style practice on privacy frameworks. · In class: Hands-on lab: research a data-access issue and evaluate its equity with AI.
Module 2: Algorithms, Automation & AI Foundations — 20% of course hours · An Algorithm & AI Ethics Analysis ReportBefore: Watch the algorithm & automated-bias lectures; resolve exam-style transparency matrices. · In class: Hands-on lab: audit an automated profiling system for bias with AI-assisted mockups.
Module 3: Digital Networks, Infrastructure & Globalization — 15% of course hours · Global Infrastructure Continuity ReportBefore: Watch the digital-divide and network-topology lectures; answer scenario questions. · In class: Hands-on lab: map infrastructure impact in developing regions with AI geospatial tools.
Module 4: Robots, Extended Realities & Emerging Tech (HL Integrated) — 15% of course hours · A Robotics & Automation Socioeconomic Impact MatrixBefore: Watch the automation and deep-learning lectures; analyze labor-displacement case studies. · In class: Hands-on lab: map an industry automation transition with AI mapping tools.
Module 5: Global Challenges & Policy Interventions — 15% of course hours · Legislative Policy Briefing DocumentBefore: Watch the tech-waste and cross-border surveillance lectures; draft mock regulatory responses. · In class: Hands-on lab: roleplay a regulation panel and pressure-test frameworks with AI.
Module 6: The Internal Assessment (IA) Digital Society Inquiry — 15% of course hours · Final Inquiry Portfolio (report + multimedia presentation)Before: Record presentation voiceovers; compile source-verification matrices. · In class: Hands-on lab: refine the inquiry portfolio with AI-assisted formatting and citations.

Foundational & intermediate electives

Introduction to Computational Logic & AI Prompt EngineeringSemester · Prerequisite: None

Frameworks: Foundational elective · computational logic without syntax pressure · cloud architecture · prompt engineering for research · digital literacy

Assessment: Understanding-first, AI allowed · portfolio + project · no terminal exam

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

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Module 1: Logic Foundations — Variables & Loops — Weeks 1–6 · A Logic Trace JournalBefore: Watch the logic-foundations lecture; answer exam-style practice questions. · In class: Hands-on lab: trace and predict program output with AI assistance.
Module 2: Functions & Cloud Architecture — Weeks 7–12 · A Refactored Logic & Cloud MapBefore: Watch the functions & cloud lecture; answer exam-style practice questions. · In class: Hands-on lab: refactor a program and map its cloud architecture with AI assistance.
Module 3: Prompt Engineering for Academic Research — Weeks 13–18 · A Research Prompt ToolkitBefore: Watch the prompt-engineering lecture; answer exam-style practice questions. · In class: Hands-on lab: build and test a research prompt library.
Module 4: Digital Literacy & Responsible AI — Weeks 19–24 · A Digital Literacy Field GuideBefore: Watch the digital-literacy lecture; answer exam-style practice questions. · In class: Hands-on lab: fact-check AI output and audit a source for bias.

AI Ethics, Law, & Digital CitizenshipSemester · Prerequisite: None

Frameworks: Humanities/tech hybrid · zero code · AI copyright, data privacy, algorithm bias, surveillance & automation

Assessment: Understanding-first, discussion & debate · portfolio · no programming

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

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Module 1: Data Privacy & Algorithm Bias — Weeks 1–4 · A Privacy & Bias Case AnalysisBefore: Watch the privacy & bias lecture; answer exam-style practice questions. · In class: Hands-on lab: analyze an algorithm for bias and debate its privacy tradeoffs.
Module 2: AI Copyright & Intellectual Property — Weeks 5–8 · A Copyright Position PaperBefore: Watch the AI copyright lecture; answer exam-style practice questions. · In class: Hands-on lab: debate an AI copyright case and draft a position.
Module 3: Surveillance, Facial Recognition & Civil Liberties — Weeks 9–12 · A Surveillance & Rights EvaluationBefore: Watch the surveillance lecture; answer exam-style practice questions. · In class: Hands-on lab: evaluate a surveillance scenario and weigh security against rights.
Module 4: Automation & the Future of Work — Weeks 13–16 · An Automation Impact ReportBefore: Watch the automation & work lecture; answer exam-style practice questions. · In class: Hands-on lab: research an automation case and map its human impact.

Java Programming & UX/UI DesignSemester · Prerequisite: Introduction to Computational Logic & AI Prompt Engineering

Frameworks: Intermediate elective · AI-assisted Java (generate, debug & audit) · UX/UI design · bridge to AP Computer Science A

Assessment: Understanding-first, AI allowed · project-based · portfolio + demo

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

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Module 1: Java Foundations with AI Assistants — Weeks 1–6 · A Java Foundations NotebookBefore: Watch the Java-foundations lecture; answer exam-style practice questions. · In class: Hands-on lab: scaffold and debug a Java program with an AI assistant.
Module 2: Data Structures & Program Design — Weeks 7–12 · A Structured Java AppBefore: Watch the data-structures lecture; answer exam-style practice questions. · In class: Hands-on lab: refactor and test a Java app with AI assistance.
Module 3: UX/UI Prototyping & System Workflows — Weeks 13–18 · A UX Prototype & User Flow MapBefore: Watch the UX/UI lecture; answer exam-style practice questions. · In class: Hands-on lab: prototype an interface and map a workflow with AI-driven design tools.
Module 4: Product Design & User Evaluation — Weeks 19–24 · A Product Demo & UX EvaluationBefore: Watch the product-design lecture; answer exam-style practice questions. · In class: Hands-on lab: run a usability test and synthesize feedback with AI.

Data Visualization & AI AnalyticsSemester · Prerequisite: None

Frameworks: Business/tech hybrid · spreadsheets & dashboards · AI analytics · zero hard programming

Assessment: Understanding-first, AI allowed · portfolio of dashboards · no database code

Pedagogy: Flipped classroom: lectures + exam-style practice questions at home · daily hands-on labs, AI tooling, and collaboration in class · Bloom's taxonomy

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Module 1: Data Storytelling & Chart Design — Weeks 1–4 · A Chart & Infographic SetBefore: Watch the data-storytelling lecture; answer exam-style practice questions. · In class: Hands-on lab: build charts from a real dataset and refine their clarity.
Module 2: Spreadsheets & Dashboards — Weeks 5–8 · An Interactive DashboardBefore: Watch the dashboards lecture; answer exam-style practice questions. · In class: Hands-on lab: build an interactive dashboard from a dataset.
Module 3: AI Analytics & Pattern Discovery — Weeks 9–12 · An AI Insights ReportBefore: Watch the AI analytics lecture; answer exam-style practice questions. · In class: Hands-on lab: use an AI analytics tool to surface and verify insights.
Module 4: The Data Storytelling Project — Weeks 13–16 · A Data Story PresentationBefore: Watch the capstone lecture; answer exam-style practice questions. · In class: Hands-on lab: build and present a complete data story.

Teaching Portfolio

Luka (Yee) Thao

Digital Transformation Solutions

Renaissance Learning

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Jane Addams School for Democracy

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