Department of Computer Science, Lehman College
Welcome to the Immersive Engineering Lab! This course is designed to equip students with the practical skills needed to succeed in an AI-driven tech industry. Moving beyond just coding, this class emphasizes systems design and architecture to provide a holistic understanding of modern full stack development. Students will learn to conceptualize, build, and deploy applications across client surfaces—web, mobile, cross-platform, CLI, and chat (LLM)—backed by servers, APIs, databases, and cloud infrastructure, while leveraging AI to enhance productivity and quality.
By the end of this course, students will:
- Understand the core components of modern full stack applications, including client surfaces (web, mobile, cross-platform, CLI, and chat/LLM), servers, APIs, databases, and cloud infrastructure.
- Develop systems architecture diagrams and explain the rationale behind design decisions.
- Gain hands-on experience building projects collaboratively using industry-standard tools and workflows.
- Learn how to integrate AI tools into the development process, focusing on testing, QA, and automation.
This course is about preparing you for the real world. The tech landscape is changing rapidly, and understanding how to design, build, and collaborate in an AI-driven environment will give you a competitive edge. You'll build three products over the semester—for yourself, for a business or entrepreneur in your network, and one of your own—practicing discovery, architecture, and responsible AI use along the way. Be curious, ask questions, and dive deep into the projects—your portfolio (and your network) will thank you later!
- Weekly Meetings: 2 sessions (1.5 hours each)
- Content Day (Monday): Architecture, surfaces, discovery, and responsible AI concepts—context setting and deep dives that inform what you build next.
- Lab Day (Wednesday): Hands-on building, PR/code review practice, and progress on your current product.
- Active Learning: Class time is dedicated to building projects and working through challenges collaboratively. Lectures are assigned as homework via readings or recorded videos.
- Spiral Curriculum: Architecture, responsible AI practices, and production deployment aren't one-time topics—they return each time you build something new, at increasing depth.
- Flexible Labs: Creating deliverables to showcase on GitHub, such as functional prototypes, architecture diagrams, and documentation.
Students will use the following tools throughout the course:
- GitHub: For version control, pull requests, and project collaboration.
- Slack: For communication and peer support.
- AI Tools: Cursor, GitHub Copilot, Claude, and similar assistants for prompting, agents, coding, debugging, and QA—used responsibly and reviewed like any other contribution.
- Development Frameworks: Next.js as the shared example stack, with exposure to CLI scripting, mobile/cross-platform tooling, and chat/LLM interfaces (including MCP-style tool connections) depending on your product's surface.
- Cloud Platforms: Hands-on experience with cloud infrastructure for deployment, environments, and scalability.
- Introduction to core components of modern full stack applications:
- Front-End: Web, mobile, cross-platform, CLI, and chat (LLM).
- Back-End: Servers and APIs.
- Databases: Relational and NoSQL databases (PostgreSQL, MongoDB).
- Cloud Infrastructure: Basics of deployment, scaling, and serverless functions.
- Using AI for code generation, debugging, and optimization.
- Understanding AI's role in systems design and how to work alongside AI tools effectively.
- Responsible AI engineering: pull requests, code review, automated testing, evals, and human-in-the-loop practices.
- Connecting tools and services (including MCP-style integrations) to extend what a product or workflow can do.
- Identifying problems worth solving: for yourself, for a business or entrepreneur in your network, and for your own product or venture.
- Discovery conversations and scoping a real problem into an architecture diagram and README-as-spec.
- Mapping workflow pain points to lightweight, AI-enabled solutions.
- Communicating with stakeholders, delivering demos, and writing handoff documentation—skills that translate to product and tech consulting/support roles.
- Agile workflows: Sprints, standups, and task management.
- Version control best practices: Git and GitHub collaboration.
- Communication and accountability through online platforms (Slack).
- Systems architecture principles.
- Diagramming and whiteboarding exercises for design planning.
- Writing effective documentation and README files for projects.
Fall 2026 meets Monday (Content) and Wednesday (Lab). First class: Mon Aug 31. The semester follows a spiral curriculum—architecture, responsible AI, and deployment thinking return each week as students build three products: one for themselves, one for a business or entrepreneur in their network, and one of their own.
| Week | Content Day (Monday) | Lab Day (Wednesday) | Pre-Class / Resources |
|---|---|---|---|
| 1 (Aug 31) | Course Overview (completed Aug 31) | SDLC & three-product arc; responsible AI ground rules (PRs, review, guardrails); AI-enabled workflows (Cursor, Copilot, prompting, agents) — first repo & PR with AI assistance | AI Capabilities & Limitations, AI Fluency for Students |
| 2 (Sep 7) | No Class (Labor Day) | Full stack architecture spine (client → API → data → deploy); surfaces landscape (CLI, web, mobile, chat/LLM); SDLC walkthrough | Instructor Looms (TBD) |
| 3 (Sep 14) | Discovery & problem–solution fit; scoping problems to a stack diagram | Product 1 kickoff: README-as-spec, architecture draft, AI-assisted planning | — |
| 4 (Sep 21) | No Class | Product 1 build: thin vertical slice; intro testing & eval mindset with AI-generated code | — |
| 5 (Sep 28) | Surface exposure — CLI & workflows; same architecture, CLI as client | Product 1 build; PR & code review practices | — |
| 6 (Oct 5) | Surface exposure — web (Next.js example); same architecture, browser as client; preview deploy intro | Product 1 build; ship a preview environment | Next.js Learn (optional) |
| 7 (Oct 12) | No Class (Columbus Day / Indigenous Peoples Day; Tue Oct 13 follows Monday schedule) | Product 1 build continued | — |
| 8 (Oct 19) | Surface exposure — mobile & cross-platform; same architecture, mobile as client; architecture recap | Product 1 demo, retrospective & peer feedback | — |
| 9 (Oct 26) | Surface exposure — chat/LLM & tool connections (MCP-style); AI workflows inside products | Product 2 kickoff: discovery with a business or entrepreneur; workflow mapping | — |
| 10 (Nov 2) | Data & API patterns (surface-agnostic); consulting framing — from workflow pain to solution brief | Product 2 scoping, architecture & consulting-shaped deliverable plan | — |
| 11 (Nov 9) | Responsible AI engineering: PRs, automated testing, evals, human-in-the-loop | Product 2 build | GitHub Copilot (optional) |
| 12 (Nov 16) | Production deployment: env, secrets, shipping, monitoring basics | Product 2 build | — |
| 13 (Nov 23) | Consulting & product deliverables; Product 2 demo & partner feedback | No Class (Thanksgiving week) | — |
| 14 (Nov 30) | Product 3 discovery; SDLC iteration — maintaining and evolving what you ship | Product 3 kickoff: scope + architecture draft | — |
| 15 (Dec 7) | Full stack + AI review: portfolio, final QA, production checklist | Product 3 build & polish | — |
| 16 (Dec 14) | Capstone: all three products — review, submission & peer reviews | — | — |
| 17 (Dec 15–21) | Finals Week | Finals Week | — |
- Attendance: Regular attendance is critical for hands-on labs and collaborative exercises.
- Project Delivery: Graded across the three products (for yourself, for a business/entrepreneur in your network, and your own), based on GitHub commits, functionality, discovery/architecture artifacts, and demo quality.
- Participation: Active engagement through Slack, peer support, PR/code reviews, and class discussions.
- All three products must be hosted on GitHub with clear documentation (e.g., a README file).
- Deliverables include working prototypes plus supplementary artifacts: architecture diagrams, README-as-spec, and—for Product 2—evidence of discovery with a real stakeholder and their feedback.
- Use of AI tools should be visible and reviewable (e.g., in PRs and commit history), not hidden or unreviewed.
- No specific language or framework required; basic programming logic (e.g., Java or Python) is helpful.
- Familiarity with foundational web concepts (HTML/CSS) is helpful but not required—this course spans CLI, web, mobile, and chat/LLM surfaces.
- Curiosity about using AI tools responsibly as part of your engineering workflow.
To support your learning, you will have access to:
- Recorded lectures, pre-class videos (e.g., AI fluency courses, instructor Looms), and reading materials.
- Sample projects and templates to kickstart development across different surfaces.
- Online forums for peer collaboration and troubleshooting.
This course observed and upholds the CUNY Policy on Academic Integrity (Accessible Plain Text). Each student in this course is expected to abide by this policy. Any work submitted by a student in this course for academic credit will be the student's own work. Collaboration is allowed where assignments are designated as group projects.
You are encouraged to study together and to discuss information and concepts covered in lecture and the sections with other students. You can give "consulting" help to or receive "consulting" help from such students. However, this permissible cooperation should never involve one student having possession of a copy of all or part of work done by someone else, in the form of an e-mail, an e-mail attachment file, a diskette, or a hard copy.
Should copying occur, both the student who copied work from another student and the student who gave material to be copied will both automatically receive a zero for the assignment. Penalty for violation of this Code can also be extended to include failure of the course and University disciplinary action.
During examinations, you must do your own work. Talking or discussion is not permitted during the examinations, nor may you compare papers, copy from others, or collaborate in any way. Any collaborative behavior during the examinations will result in failure of the exam, and may lead to failure of the course and University disciplinary action.
In compliance with CUNY policy (PDF) and equal access laws, I am available to discuss appropriate academic accommodations that may be required for student with disabilities.
We understand that our members represent a rich variety of backgrounds and perspectives. The Computer Science department is committed to providing an atmosphere for learning that respects diversity. While working together to build this community we ask all members to:
- share their unique experiences, values and beliefs
- be open to the views of others
- honor the uniqueness of their colleagues
- appreciate the opportunity that we have to learn from each other in this community
- value each other's opinions and communicate in a respectful manner
- keep confidential discussions that the community has of a personal (or professional) nature
- use this opportunity together to discuss ways in which we can create an inclusive environment in this course and across the CUNY community