Just as a math teacher needs deep mathematical understanding, AI educators need mastery of the concepts they teach. Teachers are at the heart of learning, and they can only guide students effectively when they possess real expertise in Artificial Intelligence, Prompt Engineering, and Python.
Our AI Teaching Bootcamps give educators that depth of understanding—building the technical fluency, confidence, and instructional clarity needed to teach AI with rigor and fidelity.
Our AI Teaching Bootcamps are rigorous, instructor-led learning experiences where educators take the full course themselves—mastering every concept, project, and skill they will later teach. Designed for teachers of any background who love teaching, love learning, and embrace a growth mindset, these bootcamps build deep expertise in Artificial Intelligence, Prompt Engineering, and Python and equip educators with the knowledge, skills, and confidence to deliver high-quality AI instruction in their classrooms.
Our bootcamps help districts build a sustainable, scalable AI teaching capacity—just as they do in other core subjects. Rather than relying on a small number of AI specialists, districts can develop a broad cohort of trained educators who deliver consistent, high-quality AI instruction and strengthen their programs year after year.
If a teacher who completes an AI Teaching Bootcamp is unable to teach the course they were trained for in the following school year, for any reason, the district may send a replacement educator to a future bootcamp at no additional cost—ensuring uninterrupted, district-wide AI teaching capacity.
The best way for teachers to learn.
Bootcamps are taught by expert AI practitioners with advanced expertise in Generative AI, Prompt Engineering, and Python, paired with years of classroom experience. These instructors understand both the complexity of emerging technologies and the realities of K–12 instruction, creating a supportive and effective learning environment for every teacher.
Bootcamps are delivered in collaborative cohorts of teachers from diverse backgrounds and locations, creating a supportive learning community grounded in best practices for professional development. In this shared environment, educators learn together, exchange insights, and strengthen one another’s growth. After completing the bootcamp, teachers are encouraged to continue collaborating through ongoing professional learning communities.
Our AI instructors guide and advocate for teachers throughout the learning experience, bringing deep expertise in AI and Python along with extensive classroom teaching experience. We provide multiple opportunities throughout the school year for teachers to progress and complete their professional learning. And because we understand the realities of teaching schedules, our bootcamps are delivered in manageable segments—giving educators time to practice, apply, and teach what they’ve learned before moving on to the next stage.
Learning the content, and how to teach it.
For every course in the AI curriculum, teachers complete the entire course firsthand, gaining foundational AI knowledge, advanced Prompt Engineering and Python skills, and full mastery of each concept, project, and assessment they will later teach.
Teachers also learn how to deliver effective AI instruction using the integrated teaching platform. Bootcamp instructors model high-quality lessons, demonstrate best practices, and guide teachers step-by-step through each tool and scaffold. By experiencing the course as students first, educators gain the clarity, confidence, and instructional skill needed to lead their own AI classrooms.
Teachers finish the bootcamp fully prepared to lead their AI course—having mastered the curriculum, internalized the learning sequence, and built the instructional skill needed to guide students through every lesson with clarity and confidence.
Teachers move beyond surface-level familiarity to develop strong skills in Generative AI, Prompt Engineering, and Python programming, building the deep technical knowledge that equips them to teach advanced AI concepts with precision and confidence.
Through hands-on practice and modeled instruction, teachers build the confidence and self-efficacy to deliver high-quality AI lessons, choosing the right tools, strategies, and scaffolds to support every learner.
Upon completion of the bootcamp, teachers earn industry-recognized certifications that validate their mastery of AI and Python, demonstrating their full readiness to lead high-quality AI instruction in the classroom.
AI Foundations with Python is the launchpad of our Applied AI & Data Science Pathway—a highly interactive, project-driven course designed specifically to prepare students for the Python Institute’s PCEP certification while equipping them with the foundational programming skills used to build real-world AI tools and models. Using Python, the world’s leading language for AI and machine learning, students build confidence with syntax, program structure, data types, operators, input/output handling, conditionals, loops, functions, debugging, and error handling—core competencies required for PCEP success.
Throughout the course, students apply their skills through industry-inspired, AI-focused projects that challenge them to build rule-based decision bots, logic-driven recommendation tools, simple classifiers, and automated scripts—hands-on experiences that mirror how real AI systems process information and make decisions. These experiences help students understand how AI models evaluate data, follow logic, and generate structured outputs. By the end of the course, students can write correct and efficient Python code, solve problems algorithmically, document their workflows, and clearly explain how introductory AI systems work—building the essential foundation they need to advance into Course 2: AI Application Development with Python, where they will begin creating full AI-powered applications used across today’s digital industries.
AI Application Development with Python is the second course in the Applied AI & Data Science Pathway—a rigorous, project-driven experience that advances students from foundational Python programming to building full AI-powered applications. Designed to prepare learners for the Python Institute’s PCAP certification, this course deepens students’ understanding of Python’s data structures, modular design, object-oriented principles, and application architecture, giving them the tools needed to create real, production-style AI programs.
Students explore how modern AI applications are built and deployed by learning to work with external files and datasets, design reusable modules, implement functions and classes, manipulate structured data, and interact with APIs and cloud-based AI services. They apply these skills by creating intelligent applications such as conversational agents, data-driven decision tools, text-processing utilities, and AI-integrated apps that leverage image, speech, or language models.
Throughout the course, learners follow an industry-standard development workflow—planning, prototyping, testing, debugging, refining, and documenting—building the habits required for real-world software engineering. By the end of the course, students can architect multi-file Python applications, integrate AI services, process and transform data, design modular systems, and clearly explain how their AI-powered applications work internally. These experiences prepare them for Course 3: AI Data Science & Automation, where they will apply Python to real-world data, analytics, and automation workflows.
AI Data Science & Automation is the third course in the Applied AI & Data Science Pathway—a rigorous, project-driven experience that teaches students how to use Python and foundational SQL to analyze real-world data, generate insights, and automate complex workflows. Designed to prepare learners for the Python Institute’s PCED certification, this course develops mastery in data processing, exploratory data analysis, visualization, automation scripting, relational data concepts, and computational statistics—the essential skills that power modern data science and AI systems.
Students work with industry-standard libraries such as pandas, NumPy, and matplotlib, while also learning how data is stored, retrieved, and combined using SQL. They practice querying relational tables, writing basic SQL commands, filtering and aggregating data, and performing simple joins to prepare meaningful datasets for analysis. Throughout the course, students build automated dashboards, insight-generating analytics tools, SQL-powered ETL workflows, and data-processing scripts that clean, transform, and organize large datasets.
By the end of the course, students can analyze and interpret complex datasets, work confidently with relational data sources, automate repetitive workflows, visualize trends, apply statistical reasoning, and explain how structured data pipelines support AI decision-making—fully preparing them for Course 4: Applied Machine Learning & AI Systems, where they will train and evaluate real machine learning models.
Applied Machine Learning & AI Systems is the final capstone course in the Applied AI & Data Science Pathway—a rigorous, project-driven experience that teaches students how to use Python, SQL, and core data-analysis techniques to build, train, evaluate, and interpret machine learning models. Designed to prepare learners for the Python Institute’s PCAD certification, this course deepens mastery in data acquisition, relational databases, feature engineering, statistical reasoning, model evaluation, and responsible AI practices—the essential domains that define modern data analysis and machine learning workflows.
Students use industry-standard tools such as pandas, NumPy, scikit-learn, and matplotlib, while learning how to retrieve, clean, join, and transform structured datasets from SQL databases to prepare them for modeling. Projects challenge students to design end-to-end data and machine learning pipelines that reflect how organizations build predictive analytics systems today.
By the end of the course, students can acquire data from multiple sources, prepare and analyze structured datasets, train and evaluate ML models, interpret their predictions, apply statistical reasoning, communicate results to technical and non-technical audiences, and explain how data-driven AI systems support decision-making—demonstrating the full set of competencies required for the PCAD certification.
This introductory Python course lays the foundation for learning to program by teaching students the core concepts that underpin all AI development. Through hands-on, project-based activities, students explore variables, logic, conditionals, loops, functions, and essential libraries while writing programs that automate tasks and solve problems. The course blends technical rigor with accessible learning, building the fluency and confidence students need to advance into the next course in the sequence and eventually into high school AI and data science learning.
This course builds on the foundational Python skills developed in Intro to Python Coding 1, strengthening students’ readiness for future AI and data science learning. Through hands-on, project-based coding, students deepen their understanding of program structure by working with lists, loops, functions, parameters, return values, and more advanced data handling. They learn to organize code for efficiency, break problems into reusable components, and write programs that analyze and respond to information. These experiences prepare students for the rigor of high school Python, AI, and data science courses and support their progression into the Applied Artificial Intelligence & Data Science Pathway.