Professional studies · Digital Engineering certificate · SCHEV-certified
Software Engineering
Build software with AI as your pair, and prove everything you ship. Two AI tracks, one shared foundation, a capstone in your own field, and a named instructor. Self-paced milestones with live classes every week.
Designed for two groups of learners. Same path, same certificate; your experience sets your pace.
Working professionals staying competitive in the AI era
Testers, analysts, developers and coordinators whose job is changing under them. Add the AI skills teams now expect, on your schedule.
Live classes on evening and weekend schedules, recorded if you miss one
Leave with a deployed project, automated tests in CI, and a defended capstone
Starting a career in tech
Career changers, recent graduates and students. No technical background assumed: every topic is taught concepts first, with AI doing the typing and you doing the verifying.
Your first project ships in the first milestone; your capstone is built in an industry you already know
Roles you qualify for: QA Analyst, QA Engineer, Web Developer
The program
The path to your certificate
1Stage 1Shared foundation4 milestones · 96 hours
2Stage 2Engineering core5 milestones · 140 hours
3Stage 3Your track1 milestone · 20 hours
4Stage 4Capstone28 hours, defended to a panel
How long it takes is up to you. You complete every milestone; how quickly depends on your experience, what you already know, and the hours you put in each week.
How you'll learn
Learn it, practice it, prove it, move on
1
Learn
Self-paced modules: short recorded lessons and hands-on labs.
2
Practice
Real deliverables in your own field, reviewed by your instructor.
3
Prove it
A proctored exam or a rubric-graded project. Three attempts included, no retake fees.
4
Move on
Pass, and the next milestone unlocks. The capstone closes the program.
Named instructor of recordLive classes every week, recordedOffice hours by topicProgress check every two weeks
Both tracks share the foundation and the core. Your track shapes your specialization milestone and your capstone. You declare it in the second milestone.
1Milestone 1 · Shared foundation · F1How Software Gets BuiltHow the internet, the SDLC and delivery methods work, where AI sits in every phase, and your first live page.
What you'll learn
How the internet, cloud and applications actually work, and the SDLC from requirements to release
Agile, Waterfall and hybrid: when and why, and where AI now sits in each phase
The AI work loop, hands-on: frame, give context, prompt, verify, own, with one confident wrong answer you must catch
What you'll build
Your first ship: a one-page site prompted into existence with an AI assistant and published live from your GitHub repo
2Milestone 2 · Shared foundation · F2Roles & the AI-Augmented TeamWho does what on a software team, how AI changes each seat without removing it, and the six tracks. You declare your track and passion domain here.
What you'll learn
Product owner, project manager, developer, QA, and the lenses around the build
The AI-augmented team: same roles, more output per person; the 2026 career landscape
How large language models actually work: tokens, context, why they are confidently wrong
What you'll build
A one-page role reflection plus your declared track and passion domain, the field every project from here to the capstone can live in
3Milestone 3 · Shared foundation · F3Requirements CraftElicitation, user stories with testable acceptance criteria, feature mapping, and catching what AI gets wrong in a requirements draft.
What you'll learn
Elicitation: interviews, workshops, observation, on a scripted client with planted ambiguities
User stories and acceptance criteria specific enough that a test could be generated from them
AI-drafted requirements, and how to catch what the AI invented
What you'll build
Five polished user stories with acceptance criteria for a product in your passion domain, entered in Jira
4Milestone 4 · Shared foundation · F4Agile in PracticeCeremonies, backlog grooming, estimation, PI planning, a recorded sprint simulation, responsible AI use at work, and your Passion Pitch in role.
What you'll learn
Sprint ceremonies, backlog grooming, prioritization and estimation
PI planning: how large organizations plan quarters
Responsible AI use at work: what never goes into an AI tool, disclosing AI use, verifying before trusting
What you'll build
A groomed Jira board with a sprint created, your personal AI-use policy, and a recorded readout delivered in your track role
Stage 2 · Engineering core 5 milestones
5Milestone 5 · Engineering core · E1Testing Fundamentals & the Test CycleWhy testing exists, test types and design, the defect lifecycle in Jira, a full cycle on a demo app, writing test intent for AI to expand, and API testing basics.
What you'll learn
Test types from milestone to UAT, test design techniques, Given/When/Then, and the defect lifecycle
Test intent vs. test steps: write the intent, let AI generate the cases, judge them against the requirement
API testing basics with Postman: send a request, assert status and body, save a collection
What you'll build
An executed test cycle on the demo app: 20 cases run, defects logged with reproduction steps, a summary report, and your first API collection
6Milestone 6 · Engineering core · E2SQL for Backend ValidationSQL in a testing context: does the database agree with what the UI claims? Queries are AI-drafted and hand-verified against a number you already know.
What you'll learn
SELECT, WHERE, ORDER BY, aggregates, and joins in a testing context
"UI says X, does the database agree?" validation patterns
AI-assisted query writing, verified against a row count or value you already know
What you'll build
Ten queries proving or disproving stated UI behavior against the case database
7Milestone 7 · Engineering core · E3Build → Verify: Code with AISet up the team's AI coding tools, direct AI to build a small program from your own spec in JavaScript, read every line, test it, and pass a proctored no-AI practical.
What you'll learn
VS Code with GitHub Copilot, Cursor and Claude Code set up the way a team would, agent mode used once with you reviewing every change
Code concepts through the build, in JavaScript: types, control flow, loops, functions
Reading AI code line by line, hallucination patterns, and writing tests against your own build
What you'll build
An AI-built mini program from your own spec, a log of three AI errors you caught and fixed, a reviewed pull request, and a passed no-AI practical
8Milestone 8 · Engineering core · E4Build → Verify: Front-End with AIHTML, CSS and JavaScript and the DOM, a real page built with AI and modified by hand, deployed to a real URL, one AI feature added and evaluated, and testing a peer's build.
What you'll learn
HTML/CSS/JS structure and reading the DOM (it feeds your locators later)
Deploy to a real URL on Vercel with a preview per pull request, plus a short tour of where a backend actually lives
Add one AI feature through a faculty-hosted proxy, evaluate ten outputs against a rubric, and try one prompt-injection input
What you'll build
A live passion-domain page on Vercel with one AI feature and its evaluation sheet, plus a defect report on a classmate's build
9Milestone 9 · Engineering core · E5Automation: Playwright, Git & CIBrowser automation in Playwright with JavaScript, Git for teams, one Selenium literacy session, a CI run you built, and agentic testing as awareness.
What you'll learn
Playwright in JavaScript: locators, auto-waiting, codegen from acceptance criteria, reviewing generated tests
Git for teams: branch, pull request, review, merge; one Selenium literacy session so enterprise stacks are familiar
CI built, not shown: a GitHub Actions workflow that runs your tests and API collection on every push
What you'll build
Five Playwright tests on your own build and the demo app, your API collection wired into CI, and one green CI run on your repo
Stage 3 · Your track 1 milestone
10Milestone 10 · Your track · T-DEVAI for Software DevelopersThe spec-first build loop as the job, front-end depth, and a deployed app with a template backend endpoint and your AI feature carried forward.
What you'll learn
The spec-first build loop end to end; reading and correcting AI code as the job
A faculty backend template you add one endpoint to, and an agentic coding workflow with you as the reviewer
What you'll build
Two complete build-loop artifacts (spec, build, tests, "what AI got wrong" log), one deployed on Vercel
Stage 4 · Capstone defended to a faculty panel
11Milestone 11 · Capstone · CapstoneThe Builder CapstoneBuild it, deploy it, defend it: a recorded presentation and a faculty panel, then resume, portfolio and mock interviews.
What you'll learn
Written spec, the build (AI-directed, hand-modified), test plan, Playwright checks in a green CI run
A README with an "AI tools used and what I changed" section
Presenting in role: demo plus three decisions where you overrode the AI; mock interviews with a live code-reading screen
What you'll build
Your deployed capstone, its recorded defense (a portfolio asset), a polished resume and a live portfolio
One milestone at a time, in order. Milestone content is maintained by faculty and may be refined between cohorts. Clock hours, assessments and program requirements are stated in the school catalog and your enrollment agreement.
Questions
Questions people ask
How long does it take?
You complete every milestone; how quickly depends on your experience, what you already know, and the hours you put in each week.
Do I have to attend classes at set times?
No, but live instruction is there for you every week. Join live or watch the recording, and drop into topic office hours whenever you need help. The required commitments are a short progress check every two weeks, proctored assessments you book when you're ready, and your capstone presentation.
When can I start?
New groups begin the first of every month. Enroll anytime, pick your start month, and complete guided onboarding before your start date.
Upon completion you receive a certificate that names both your Digital Engineering program and your chosen specialization, recognized by the State Council of Higher Education for Virginia (SCHEV). Track names describe the specialization focus and career positioning of each concentration; approved outcome roles are QA Automation Engineer, QA Analyst, SDET and Full-Stack Developer. Program outcomes, hours and credentials are listed in the school catalog. Individual employment results vary; no specific job title or salary is guaranteed.