AI Native Application Development - MERN
Course Description
This 15-week course covers the dynamic world of full-stack MERN (MongoDB, Express, React, Node) software development through the lens of Agentic AI. It is designed for learners seeking to sharpen their full-stack development skills while embedding agentic workflows that transform them into capable, AI-empowered software engineers.
Moving the focus away from language syntax alone, you will receive instruction in the foundational concepts of application development while engaging with AI frameworks in teaching mode. Throughout the course, you will use AI in three distinct roles:
- As a Socratic Tutor to master programming fundamentals. In this role, the AI is forbidden from writing code; it asks guiding questions and leads you to your own solutions.
- As an Architect to plan system designs. In this role, the AI drafts plans, schemas, and architecture documents while you act as a Product Manager, reviewing and approving its work.
- As an Agentic Builder, directing autonomous AI agents (such as Claude Code) to write, refactor, and deploy complex codebases. In this role, you act as an Engineering Manager, responsible for reviewing, auditing, testing, and approving the final commits.
Strong fundamentals of web development (HTML, CSS, and JavaScript), advanced React component architecture, backend server creation with Node.js and Express.js, database management with MongoDB, and CI/CD deployment pipelines still form the backbone of the curriculum. By the end of the course, you will possess both a deep conceptual understanding of software engineering and the ability to apply a multiplying factor to your abilities by leveraging Agentic AI.
Course Objectives
Upon successful completion of all course requirements, learners will:
- Design responsive and visually appealing web interfaces using HTML, CSS, and modern web design tools.
- Plan and design enterprise-grade MERN stack applications by writing comprehensive structural blueprints to guide AI execution.
- Build and manage robust backend systems using Node.js, Express.js, and MongoDB.
- Create a comprehensive portfolio of a full-stack project, demonstrating proficiency in the MERN stack and secure application development.
- Explore cutting-edge AI technologies within the technology domain to enhance outcomes.
- Direct and manage autonomous AI agents to seamlessly scaffold, write, and integrate complex software features.
- Develop foundational web development syntax and computational thinking using AI strictly as a conceptual Socratic tutor.
Graduates will be uniquely positioned to jumpstart their careers as AI-Native Full Stack Developers, AI Junior Engineers, and Web Developers.
Prerequisites
To be considered for this course, learners are required to meet the minimal qualifications for a Per Scholas course and must also demonstrate comprehension, critical thinking, and digital literacy skills through a baseline assessment.
Additionally, learners must complete approximately twenty (20) hours of self-study materials to prepare for the course, included here for reference: Software Development Tech Prep Work Instructions. Topics include basic HTML and CSS, and an introduction to JavaScript.
Course Materials and AI Tooling
All course materials are accessible through the Canvas Learning Management System (LMS) .
An onboarding presentation for the Workplace Simulation training you will be participating in can also be found here ; your instructors will likely go over this presentation with you during the first days of class.
As part of this course, Per Scholas provides each learner with:
- A Pro-level Claude account (claude.ai) for use with the Socratic Tutor and Architect activities.
- Access to Claude Code, the agentic coding tool used for Builder activities.
- Anthropic API credits for labs and projects in which you build AI-powered features yourself.
If provided accounts are unavailable for any reason, AI-Native activities can be completed using free-tier Claude access or another AI assistant with equivalent capabilities. Free-tier usage limits may require completing an activity across more than one session.
This course also includes the Google AI Professional badge coursework, introduced during Module 1. Your instructor will provide enrollment details.
Course Components and Grading
All assignments and assessments can be found within the Canvas LMS. The following sections describe the purpose, methodology, policies, and weight for graded material.
Description of Assignments
The following labels and acronyms will be used throughout the course:
- Labs
- Small tasks or projects that aid in skill formation and progress assessment.
- Graded as either complete/incomplete or via a rubric, depending on the complexity of the lab.
- Labs marked (AI) are AI-Native activities unique to this version of the course. Some replace a standard lab one-for-one and others are additional; each module outline states which applies.
- Knowledge-Based Assessments (KBA)
- Timed quizzes that cover factual knowledge and critical thinking skills, rather than practical usage of technical skills.
- Useful for interview practice, and general ability to confidently communicate technical knowledge.
- Skill-Based Assessments (SBA)
- Open-book, cumulative programming tasks that demonstrate practical knowledge and skill proficiency with a module’s content.
- Projects
- Week-long (or more) projects that showcase the skills gained throughout a specific section of the course.
- In-Class Participation (ICP)
- An assessment of participation throughout the course, including:
- Verbal and written communication to peers and instructors, including on module discussion boards.
- Active listening and solution-seeking through questions and independent research.
- Teamwork, such as collaboration during group activities or assignments, but also including empowering peers to meet their objectives through whatever means an individual has available.
- Punctuality and preparedness, particularly when assigned material to complete between class days.
- An assessment of participation throughout the course, including:
Grade Weights by Assignment Type
The weight of each assignment category, as well as a brief description of the category, is as follows.
| Assignment Type | Description | Count | Total Weight |
|---|---|---|---|
| Lab | Small tasks or projects that aid in skill formation and progress assessment. Graded as either complete/incomplete or via a rubric, depending on the complexity of the lab. Labs marked (AI) are AI-Native activities; where one replaces a standard lab, the replaced lab is not assigned in this course. | 38 | 22.2% |
| SBA | Open-book, cumulative programming tasks that demonstrate practical knowledge and skill proficiency with a module’s content. | 11 | 17.8% |
| KBA | Timed quizzes that cover factual knowledge and critical thinking skills, rather than practical usage of technical skills. Useful for interview practice, and general ability to confidently communicate technical knowledge. | 12 | 7.2% |
| Project | Week-long (or more) projects that showcase the skills gained throughout a specific section of the course. | 4 | 47.8% |
| ICP | An assessment of participation throughout the course, including: Verbal and written communication to peers and instructors (including on module discussion boards); Active listening and solution-seeking through questions and independent research; Teamwork, such as collaboration during group activities or assignments, but also including empowering peers to meet their objectives through whatever means available; Punctuality and preparedness, particularly when assigned material to complete between class days. | N/A | 5.0% |
| Total | Total | 65 | 100.0% |
Grade Weights by Module
The weight of each module can be broken down as follows.
| Module | Assessment | % Weight | Module Weight |
|---|---|---|---|
| 1. Google AI Professional | Lab 1.1 (AI): AI Tooling Setup and Agentic Baseline | 0.4% | 1.4% |
| Lab 1.2 (AI): Google AI Professional Coursework | 0.4% | ||
| KBA 1: Fundamentals of Generative AI | 0.6% | ||
| 2. Introduction to Version Control | Lab 2.1 (AI): Resolving Merge Conflicts with a Socratic Tutor | 0.6% | 2.3% |
| SBA 2: Version Control | 1.1% | ||
| KBA 2: Version Control | 0.6% | ||
| 3. HTML and CSS Review with Accessibility | Lab 3.1 (AI): Accessibility Audit with a Socratic Tutor | 0.4% | 2.5% |
| SBA 3: Building a Responsive and Accessible Web Page | 1.5% | ||
| KBA 3: HTML and CSS Review | 0.6% | ||
| 4. Foundations of Web Design | Lab 4.1: Exploring Advanced Figma Features | 0.6% | 4.5% |
| Lab 4.2 (AI): From Figma Export to a CSS Baseline with a Socratic Tutor | 0.9% | ||
| Lab 4.3: Developing with Bootstrap | 0.6% | ||
| SBA 4: Design and Development | 1.8% | ||
| KBA 4: Foundations of Web Design | 0.6% | ||
| 5. JavaScript Review | Lab 5.1: Variables and Functions | 0.6% | 3.6% |
| Lab 5.2: Array Manipulation | 0.6% | ||
| Lab 5.3 (AI): Socratic Debugging of Loops and Conditionals | 0.5% | ||
| SBA 5: Task Management App | 1.3% | ||
| KBA 5: JavaScript Review | 0.6% | ||
| 6. The Document Object Model | Lab 6.1: Dynamic Content Creation | 0.6% | 3.6% |
| Lab 6.2 (AI): Interactive Registration Form with a Socratic Tutor | 0.6% | ||
| SBA 6: The Document Object Model | 1.8% | ||
| KBA 6: DOM Concepts | 0.6% | ||
| 7. TypeScript and Advanced JavaScript | Lab 7.1: TypeScript and Object-Oriented Programming | 0.6% | 3.6% |
| Lab 7.2 (AI): Promises, Interfaces, and the Socratic Tutor | 0.6% | ||
| SBA 7: TypeScript and Advanced JavaScript | 1.8% | ||
| KBA 7: TypeScript and Advanced JavaScript | 0.6% | ||
| 8. Project: HTML, CSS, and JavaScript | Lab 8.1 (AI): Architecting Your Project with Claude | 0.5% | 9.0% |
| Project: HTML, CSS, and JavaScript | 8.5% | ||
| 9. React Fundamentals | Lab 9.1 (AI): Component Creation & Props with a Socratic Tutor | 0.6% | 4.5% |
| Lab 9.2: Props and State | 0.9% | ||
| Lab 9.3: Event Handling | 0.6% | ||
| SBA 9: React Dashboard Application | 1.8% | ||
| KBA 9: React Fundamentals | 0.6% | ||
| 10. Advanced React | Lab 10.1: React Counter | 0.2% | 4.6% |
| Lab 10.2: Custom Hooks | 0.7% | ||
| Lab 10.3 (AI): Context API Implementation with an AI Architect | 0.9% | ||
| Lab 10.4: Dynamic Routing | 0.4% | ||
| SBA 10: Advanced React | 1.8% | ||
| KBA 10: Advanced React Concepts | 0.6% | ||
| 11. Project: React Development | Lab 11.1 (AI): Autonomous Component Development with Claude Code | 0.5% | 13.6% |
| Project: React Development | 13.1% | ||
| 12. Node and Express | Lab 12.1: Build a Basic Express Server | 0.7% | 3.7% |
| Lab 12.2: Server to Server Communication | 0.6% | ||
| Lab 12.3 (AI): Architect the Daily Grind API | 0.5% | ||
| SBA 12: Build a RESTful Server | 1.3% | ||
| KBA 12: Node and Express | 0.6% | ||
| 13. MongoDB Fundamentals | Lab 13.1: Connecting a Database | 0.4% | 3.7% |
| Lab 13.2 (AI): Architecting the Digital Bookshelf | 0.9% | ||
| SBA 13: Integrating a Database | 1.8% | ||
| KBA 13: MongoDB Fundamentals | 0.6% | ||
| 14. Authentication and Authorization Principles | Lab 14.1: Basic Login System | 0.6% | 4.5% |
| Lab 14.2 (AI): Secure Record Storage with an AI Builder | 0.9% | ||
| Lab 14.3: OAuth Integration | 0.6% | ||
| SBA 14: Secure Web Portal | 1.8% | ||
| KBA 14: Auth Principles | 0.6% | ||
| 15. Project: Back-end Development | Lab 15.1 (AI): Builder Agent Controllers and Test Suites | 0.5% | 9.0% |
| Project: Backend Development | 8.5% | ||
| 16. Unifying User Interfaces with Web Applications | Lab 16.1: Full-Stack Integration | 0.4% | 1.5% |
| Lab 16.2: API Flexibility | 0.6% | ||
| Lab 16.3 (AI): Generating the React Fetch Layer with a Builder Agent | 0.5% | ||
| 17. Deploying MERN Applications | Lab 17.1: CI/CD Pipelines | 0.6% | 1.2% |
| Lab 17.2 (AI): Full-Stack Deployment with a Builder Agent | 0.6% | ||
| 18. Project: MERN Full-Stack Portfolio | Lab 18.1 (AI): Pre-Deployment Refactoring Sprint | 0.5% | 18.2% |
| Project: MERN Development | 17.7% | ||
| In-Class Participation (ICP) | In-Class Participation (ICP) | 5.0% | 5.0% |
| Total | Total | 100.0% | 100.0% |
Labs marked (AI) are AI-Native activities, and are the only labs in this course that differ from the standard MERN curriculum. Where an (AI) lab replaces a standard lab, the replaced lab does not appear in the AI-Native module and is not assigned. Labs 1.1 and 1.2 are delivered through Canvas as part of Module 1 onboarding.
Assessment Policy
Learners must complete all labs, assignments, and assessments according to the schedule given by the instructor.
Please note that Per Scholas has a strict academic integrity policy. Plagiarism is considered cheating. Any learner caught plagiarizing will be automatically dismissed from the course. Plagiarism includes, but is not limited to, copying answers or assignments from another learner or a website. Learners can reference material as long as it is properly cited.
Academic Integrity in an AI-Native Course
This course requires you to use AI tools, but each activity defines the role the AI is permitted to play. Honoring those boundaries is an academic integrity requirement:
- When an activity uses the Socratic Tutor role, submitting AI-written code or AI-written explanations as your own work is a violation.
- When an activity uses the Architect or Builder roles, you must genuinely perform the review, audit, and approval work the activity requires, and your submitted artifacts (review logs, audit reports, reflections) must be your own writing.
- All major graded assignments include a check-in, code review, or technical discussion in which you must be able to explain what was built, how it works, and why decisions were made, regardless of who or what wrote the code.
Course Schedule
Learners will have five (5) days of instruction per week, from Monday to Friday.
- Class hours are from 9:00 a.m. to 5:00 p.m.
- There will be a one-hour (1) break for lunch in the middle of the day.
- There will be two (2) additional fifteen-minute (15) breaks, one before and one after lunch.
- The final hour of each day (4:00 p.m. to 5:00 p.m.) is Open Lab / Office Hours for individual guidance, advanced topics, and check-ins.
Additionally, learners are expected to dedicate up to two (2) hours daily for homework and review.
Module Schedule
The curriculum is thoughtfully divided into sections and modules, each carefully crafted to cover specific areas of expertise:
Onboarding and Section One: Front-End Development
| Module | Outcome | Synchronous Hours | Asynchronous Hours | Total Class Days |
|---|---|---|---|---|
| Learner Onboarding and Workplace Readiness | Learners will be able to navigate course resources, technology platforms, and be effective in the workplace environment. | 22 | 8 | 4 |
| Google AI Professional | Learners will be able to utilize AI tools to aid in software development tasks. | 11 | 4 | 2 |
| Introduction to Version Control | Learners will be able to manage project versions and collaborate using Git and GitHub. | 11 | 4 | 2 |
| HTML and CSS Review, with Accessibility | Learners will be able to create well-structured, accessible, responsive web pages with audited accessibility features using HTML and CSS. | 11 | 4 | 2 |
| Foundations of Web Design | Learners will be able to design high-fidelity wireframes and prototypes in Figma and implement them in web development projects. | 22 | 8 | 4 |
| JavaScript Review | Learners will be able to develop simple JavaScript programs using variables, data types, operators, control structures, and functions. | 11 | 4 | 2 |
| The Document Object Model | Learners will be able to manipulate web page content and respond to user interactions using DOM methods and events. | 16.5 | 6 | 3 |
| TypeScript and Advanced JavaScript | Learners will be able to write type-safe JavaScript code and handle asynchronous operations using TypeScript and Promises/async-await. | 22 | 8 | 4 |
| Project: HTML, CSS, and JS | Learners will lay the groundwork and build out pieces that can be used for upcoming lab activities and the capstone. | 22 | 8 | 4 |
Section Two: React Development
| Module | Outcome | Synchronous Hours | Asynchronous Hours | Total Class Days |
|---|---|---|---|---|
| React Fundamentals | Learners will be able to build React applications with reusable components. | 22 | 8 | 4 |
| Advanced React | Learners will be able to manage state and side effects using React Hooks and implement routing with React Router. | 22 | 8 | 4 |
| Project: React Development | Learners will be able to plan, build, and debug a real-world frontend React project. | 22 | 8 | 4 |
Section Three: Back-End Development
| Module | Outcome | Synchronous Hours | Asynchronous Hours | Total Class Days |
|---|---|---|---|---|
| Node and Express | Learners will be able to develop a backend server with Node.js and Express.js, including routing and middleware. | 22 | 8 | 4 |
| MongoDB Fundamentals | Learners will be able to perform CRUD operations and manage data using MongoDB and Mongoose. | 22 | 8 | 4 |
| Authentication and Authorization Principles | Learners will be able to implement secure authentication and authorization systems in web applications. | 22 | 8 | 4 |
| Project: Back-end Development | Learners will be able to develop, test, and debug a real-world server-side project using Node, Express, and MongoDB. | 22 | 8 | 4 |
Section Four: Full-Stack Development
| Module | Outcome | Synchronous Hours | Asynchronous Hours | Total Class Days |
|---|---|---|---|---|
| Unifying User Interfaces with Web Applications | Learners will be able to connect frontend React applications with backend Express servers following best practices. | 11 | 4 | 2 |
| Deploying MERN Applications | Learners will be able to build and deploy a full-stack application to hosting services. | 11 | 4 | 2 |
| Project: MERN Full-Stack Portfolio | Learners will be able to refine their projects into a portfolio and present a full-stack solution by incorporating lessons learned from throughout the course. | 38.5 | 14 | 7 |
Professional Development
Additionally, nine (9) days throughout the course are dedicated to professional development and graduation, enabling learners to effectively present a professional personal brand and demonstrate enhanced communication, emotional intelligence, and success habits. This includes AI-assisted job readiness work: polishing resumes and portfolios, generating role-aligned application materials, and simulating interviews with AI tools.
Daily Schedule
Example
The following is an example of a day in class.
| Time | Activity |
|---|---|
| 9:00 a.m. | Attendance |
| 9:05 a.m. | Daily standup |
| 9:20 a.m. | Review of previous night’s material, questions, discussion, and knowledge checks |
| 10:30 a.m. | Break |
| 10:45 a.m. | Lessons and activities for the current module, check-ins |
| 12:00 p.m. | Lunch |
| 1:00 p.m. | Continued lessons and activities for the current module, check-ins |
| 2:30 p.m. | Break |
| 2:45 p.m. | Practical problem solving, questions, discussions |
| 3:30 p.m. | Assignment of the evening’s material, questions |
| 3:45 p.m. | Reflection and consolidation, journaling |
| 4:00 p.m. | Open lab or project time; office hours for individual guidance, advanced topics, and check-ins |
| 5:00 p.m. | End of day |
Your instructor is the final authority on what each class day contains, and how that time is allocated. The above is only an example to serve as a reference and set general expectations.
Days Off
Per Scholas will be closed on the dates shared in the weekly Canvas course calendar. You are expected to work on classwork and projects during days when class is not in session, unless otherwise instructed.
Required Resources for Learners
- Compute: Intel or AMD processor, 64-bit operating system (Windows, Linux, or Mac), 16 GB RAM minimum, quad-core i5 to i7 minimum (i7 with 6 to 8 cores preferred). Chromebooks are not supported.
- Network: Reliable internet access (Wi-Fi or Ethernet).
- Additional: 100 GB of free storage, webcam, and microphone.
- Software: An AI-enabled browser (Chrome, Edge, etc.), generative AI tools (Anthropic Claude), Claude Code, Microsoft Office 365, Zoom (webcam and microphone enabled), Slack, Node.js, Express, React, MongoDB, Visual Studio Code, Git, and Jira.
Enrollment Agreement and Additional Policy Information
For all other policy information, including behavior expectations, dress code, academic integrity, accommodations, and more, please reference the Per Scholas Enrollment Agreement .
Standard Occupational Classification Codes
This course aligns with the following Standard Occupational Classification (SOC) codes:
- 15-1251: Computer Programmers
- 15-1252: Software Developers
- 15-1254: Web Developers
- 15-1255: Web and Digital Interface Designers
- 15-1211: Computer System Analysts
- 15-1243: Database Administrators and Architects
- 15-1253: Software Quality Assurance Analysts and Testers
Classification of Instructional Programs Codes
This course aligns with the following Classification of Instructional Programs (CIP) codes:
- 11.0201: Computer Programming/Programmer, General
- 11.0202: Computer Programming, Specific Applications
- 11.0205: Computer Programming, Specific Platforms
- 11.0701: Computer Science
- 11.0801: Web Page, Digital/Multimedia and Information Resources Design