Render

Goal

Career-readiness work usually disappears when Canvas access ends at graduation. Render is owned and portable, so students keep using and building it afterward. It answers the “now what?” moment at the point where students most often lose momentum, and hands them a launchpad they carry into the job search rather than leave behind.

Render is a personal learning environment, connectivism in practice: one place a student owns where goals, work, skills, and connections live together instead of scattered across a course that ends. It is anonymous by design and collects no student PII, an anonymous handle, no name, address, or email, with each student’s work saved on a district-owned backend so a wiped machine never loses it. Students set robust goals and pick one real reach job on day one, then build the environment across the AVC 248 capstone: profile, goals, job log, resume vault, skills, networking, and interview prep, every module anchored to that reach job.

Audience

Community college Digital Media Arts students finishing a creative program and stepping into the job or freelance market. They are early-career people who need structure, not a blank prompt, and somewhere to put the work they have already done. Render is built in partnership with campus Career Services, so the tool reinforces the same guidance a career advisor would give rather than competing with it.

Process

At the capstone, Render runs a gap analysis between what the student actually built and the requirements of their day-one reach job. From that it exports the student’s package: their agents and skills as prompt files, a job tracker, their tailored documents, and a personal learning plan aligned to the goals they set at the start. The student keeps the package and runs their agents in their own AI tool, such as Gemini, Claude, or ChatGPT, after graduation. Render does not get replaced by the agents: Render builds the agents.

There is also a quiet feedback loop to campus Career Services. When a student saves a job they are pursuing, Render sends one anonymous row to a Career Services Google Sheet through Apps Script, the employer name, job title, link, interest level, and program area, with no student names or IDs attached. It lets Career Services see which employers students are targeting and build internship and placement pipelines, and the student is told exactly what is shared at the moment they save.

Technology

• A single-page front end: CSS and vanilla JavaScript, no framework.
• No API key and no AI service behind the tool. Render assembles the prompt, the student runs it in whichever free AI they already have (Gemini, ChatGPT, Claude, Copilot) and pastes the result back. That removes the cost, the key, and the single point of failure, and it makes the agent visible: a student who copies the prompt can see that an agent is written instructions, not a button.
• Agents and skills: about twenty single-shot skills (resume tailoring, cover letter, gap analysis, interview questions, elevator pitch, LinkedIn, salary research, and more) plus the orchestrating agents, a career counselor, a per-job hiring committee, and an interview panel, and the student’s own job-search agent. The student runs every one of them in their own AI account, so the agents belong to them from the first week rather than at export.
• Storage: first name only, kept in the browser, no account and no PII. Because browser storage can be cleared, the exported package is the durable copy and students export throughout the term rather than once at the end.
• Google Sheets and Apps Script for the anonymous Career Services data flow.
• Export: a downloadable zip package the student owns, a self-contained dashboard, a spreadsheet job tracker, editable resume and cover-letter documents, and every agent and skill as a prompt file they run in their own AI.
• Hosting: static GitHub Pages. No backend, no server, no key to provision, nothing that has to be approved before a student can open it.

Outcomes

Render was usability tested with students in March 2026 and refined from what that surfaced. It is in pilot this fall in one section of the AVC 248 capstone, with success defined up front: whether students finish and keep their exported career agent and learning plan, whether they report still using it after the course ends, and whether Career Services receives the anonymous employer signal the tool is meant to route. Where a number is not in yet, the measure is named rather than guessed.

Status

In pilot this fall in one section of the AVC 248 capstone. The earlier design depended on district infrastructure, a hosted key, a proxy, and a backend. That support did not materialize, so the tool was rebuilt to need none of it. Students run the agents in their own free AI accounts, assemble their own repository of agents and skills, and write a reflection on what they built and why they built it that way. The constraint improved the teaching: the previous design hid the agent behind a button, and this one puts the student in the position of running it. No personally identifiable information is collected, first name only, kept in the browser, and the exported package is the student’s to keep.