Stories from peers in fields like yours.
Illustrative composites grounded in our formative interviews — what students built, what concentration they came from, and how long it took.
Read peer stories§ 00 · Opening
AI fluency for someone going into .
A program for first-generation and low-income undergraduates exploring how AI fits into a career like the one you're building — without assuming you came in with a coding background.
6 sessions7–9 hours total$0 to participateNo coding required
§ 01 · Why AI literacy matters
Using AI well takes more than a chat window.
AI tools are reshaping everyday work, from drafting and synthesis to coding, analysis, and decision support, yet using them well takes more than access to a chat window. Strong AI literacy means knowing enough to frame problems clearly, steer tools responsibly, verify outputs against evidence, and understand limits around bias, hallucination, and privacy. Across disciplines, those skills are increasingly affecting coursework, internships, interviews, and how people participate in workplaces that are experiencing heavy automation. When people only get fluent in AI through uneven informal exposure or advanced prerequisites, the gap is not just technical comfort but longer-term opportunity. Inclusive, practice-grounded learning is one way to keep AI from becoming another axis on which access diverges.
§ 02 · Student feedback
What participants have said about the experience.
“I feel much more confident in becoming ready for internships and being in the workforce because I can use AI tools to improve my efficiency, which is what my internship boss wanted to see from me last summer. I have another internship this summer and I know I can be more proactive in reaching out because I have a stronger foundation through this engaging curriculum.”
“I wish I had this tool when I was back in high school so I could work on AI research projects that had real-world impact under mentorship and collaboration.”
“I really like how accessible this platform is and how it assumes no knowledge of AI. Learning from this platform means I don't need to be afraid of other people judging me for my lack of AI knowledge.”
§ 03 · Why it could matter for you
§ 04 · See what students build
Concrete projects you could finish.
From no-code workflows to short scripts. Each one tells you the tools, the time, and the field. You pick the topic; the program is built around helping you finish it.
Browse projects§ 05 · How it works
Six checkpoints, each one ending in something you've made.
Every checkpoint runs under 90 minutes and can be done asynchronously. You hold the deliverable at the end of each one.
- 01A shared vocabularyPlain-language definitions for the terms you'll keep hearing — model, LLM, prompting.
- 02Your project ideaConnect AI to a question that already matters to you, in your own field.
- 03Scope the literatureUse AI to map what's been done, then verify the parts you're going to lean on.
- 04Build a first versionPick a no-code or minimal-code track and produce something working.
- 05Take peer critiqueShare what you built, take specific feedback, revise once.
- 06Tell the storyWrite up what you built and why, in language you can use in interviews.
§ 06 · What we built around
Four reasons students said they hadn't started — and what we did about each one.
These came up by name in our formative interviews. We list them here so you can decide for yourself whether the program is set up around the constraints you actually have.
- Barrier 01No code
No coding background
Starter projects use chat tools and templates. Coding shows up later as an option, not a requirement.
Cited as a barrier by every FGLI interviewee.
- Barrier 02$0
Cost of AI tools and subscriptions
Free access to the AI tools the program uses, provided through the program. No subscription is required to participate.
Raised by interviewees who tried paid tools and stopped.
- Barrier 03≤ 90 min / session
Time pressure during the semester
Each checkpoint runs under 90 minutes and can be done asynchronously, in pieces, around your other obligations.
Raised across the interview group, especially by students balancing work hours.
- Barrier 04Your topic
An unclear starting point
You pick the project topic from your own career area, with checkpoints and a clear next step at every stage.
The single most common reason interviewees said they hadn't gotten started.
§ 07 · Closing
Students who got an early start on AI didn't have better brains — they had better access.
We're trying to extend that access on terms that respect your time, your field, and your starting point.