AI-FYE: AI in the First Year Experience

AI literacy, as used throughout this framework, means reading, thinking, and writing using the tools, techniques, and technological systems consistent with one's peers. That definition — RTW — is not about AI as subject matter. It is about AI as the medium through which literate practice now happens. It begins with the same moves literacy has always required: reading carefully, thinking critically, writing with intention. What changes is the environment those moves are made in.

This document is itself a product of that practice. It was developed through sustained conversation with an AI system — generating design specs, working through multiple versions, validating and verifying across models — with the human author reading outputs, thinking against them, and writing through the friction at every stage. The model got the last word on the artifact you are reading now. That is not a footnote. It is the point. The document enacts what it describes.

AI-FYE is a first-year experience framework built around that premise. Many students are already using AI across multiple courses. This program makes that visible, structured, and academically productive.

Rather than adding AI instruction on top of existing courses, AI-FYE creates a shared space — the AI Salon — where the AI-assisted work students produce in their regular courses becomes the primary text. Faculty bring their assignments. Students bring their artifacts. The Salon is where we examine what happened across disciplines.

The pilot runs with 20 students, one section of AI 188, and three participating faculty — two teaching general education courses and one teaching a disciplinary introduction. All three courses share the same cohort of students for the semester.

AI 188 is the connective tissue. It does not teach AI tools in isolation. It creates a recurring space where students surface and examine the AI-assisted work they are producing in their other courses.

The course has two phases. The first five hours function as a structured orientation — the Learner's Permit (see the separate LP document for full details). Every student, and every participating faculty member, completes this together before the semester begins. It establishes a shared baseline: common vocabulary, common tooling, common documentation practices.

After the LP orientation, weeks 1–16 are Salon sessions. Students bring artifacts from their gen ed and major courses. The instructor facilitates cross-disciplinary comparison. A nursing student and an engineering student running similar prompts for different assignments produce different outputs — the differences, the surprises, and the patterns across disciplines are the curriculum.

These are your existing courses. The only structural change is coordination: two or three assignments per semester timed to land in the same week across all participating courses, so the Salon has comparable artifacts to work with. What those assignments are, how they are graded, and what they ask — that remains entirely yours.

The naming convention (Gen Ed 188, BIZ 188, EE 188, etc.) signals participation in the cohort. It does not create a new course. It signals to students and the institution that this section is part of the AI-FYE pilot.

Before the semester begins, all 20 students and all participating faculty gather for a shared orientation. The Learner's Permit is not homework — it happens here, together, in a single event.

The LP is a structured 15-exercise scaffold that establishes functional AI literacy: setting up a working environment, running a first prompt, documenting a conversation, exporting a transcript, building a basic folder structure. By the end of the session, every participant has completed the same exercises and holds the same provisional credential to proceed.

Faculty do this alongside students. That is not incidental — it is the point. The Salon works because it is a genuinely shared inquiry, not a top-down curriculum. Faculty who have done the LP have standing to participate in Salon discussions as practitioners, not just as instructors.

The Salon meets once per week for AI 188. Sessions are short — one credit means roughly one contact hour per week. The format is consistent:

The Salon does not grade the quality of AI use in other courses. It creates a space to examine it. The gen ed and major instructors retain full authority over how AI is used in their courses and how work is assessed.

AI-FYE is an AI-engaged program by design. Faculty who join have already agreed to that premise — the coordination model depends on it. Courses in the cohort are AI-forward; this is the selection condition, not a constraint imposed after the fact.

The pilot is deliberately small. Twenty students. Three faculty. One AI 188 section. The goal is to build and document a model that scales, not to maximize enrollment in year one.

Student artifacts — exported AI conversation transcripts — are stored in a shared Microsoft SharePoint environment with granular access controls. Students, participating faculty, and the AI 188 instructor each have appropriate access. Artifacts used in Salon sessions are anonymized by default unless students choose otherwise.

Scaling to a second cohort — 40 students, 2 sections of AI 188, 4 gen ed faculty — is the natural next step once the pilot model is documented. The documentation happens during the pilot: every session, every artifact set, every coordination decision becomes part of the OER framework available to other SUNY campuses.

Participating faculty join the project by joining the shared Claude Team Project where this document lives. From there, the work continues the same way this document was built: iterative conversation, generating and refining artifacts together, with the model getting the last word on each output.

That process is worth naming directly. This entire program — course structure, pilot scope, faculty roles, assessment model — emerged from a single sustained thread of reading, thinking, and writing with AI. At an early stage, the program ran AI 188 as a 2-credit course alongside a 2-credit gen ed. A reviewer flagged that as too narrow for a general education context. One prompt, one iteration, and the change propagated through the entire program design. That is not editing. That is how RTW works at scale: a reflection generates a revision that regenerates the whole.

The next artifacts to generate are the ones the pilot needs: a coordination agreement for participating faculty, a session template for the Salon, and a student-facing orientation guide. All of these will be built the same way, in the same shared space.

The pilot needs faculty who are willing to try something, document what happens, and help build the model from the inside. You do not need to be an AI expert. You do not need to redesign your course. You need to be willing to have your students' AI work surface in a shared conversation.

If that sounds like something you'd want to be part of, the next step is a single conversation — to confirm timeline, identify the coordination points in your existing syllabus, and confirm your participation in the LP orientation.

This document is part of the SUNY AI Literacy Curriculum collection. Related documents: Learner's Permit · Commercial Driver's License · AI Literacy Implementation Synthesis. All materials are open and freely adaptable under Creative Commons.