AI Literacy and Education Programs

AI Literacy Programs

Three programs, one standard

One standard runs through three programs built to fit the system New York already runs. The first is a two-year program for grades 11 and 12 through a BOCES career and technical education center. The second is a 64-credit Associate in Applied Science whose foundation core also serves as a minor. The third is an 18-month professional certificate for people who already hold a degree.

Every state writes its own rules, so a partner in another state would map this design onto its own structure, with New York as the working example.

The shared architecture

Every program splits in two. Half of each program is a shared foundation covering the basics of every subject. It teaches the ground rules, the method, evidence and data qualification, and how AI works well beyond language models. It also covers automation and agents, multi-AI practice, and the people who shaped the field. The other half is a track with its own core courses and electives, and three tracks run through all three programs.

Every AI-assisted session follows one sequence

How every learner is measured

Design elements shared by all three programs

A Track 3 learning design may include a governed course assistant, in any of the three programs. The assistant answers routine questions and sorts discussion posts under a triage policy the instructor signs, while the instructor keeps grading and all feedback that counts. It can also run in a question-only configuration that asks the learner questions and withholds answers. That configuration serves early units as a scaffold, stays out of Human First, and is withdrawn on the published schedule. If the assistant speaks in the instructor’s voice, its answers carry the reconstruction label that applies to any simulated opinion of a real person.

The people of the field

The strand on the people of the field draws its seven classes from The Minds That Bend the Machine (Puglisi, in press). The survey in the foundation covers all seven, and each becomes an elective class in the track half.

Exit targets

Exit targets rise with each program. The targets count only what the learner designs and operates after the scaffolding is withdrawn. A learner working inside a teacher-built environment has not yet shown the level that environment models (Puglisi, 2026e). They are design targets, and they become graduation standards only once HEQ is validated for that use.

Employment as evidence about the person

Each program also follows its graduates into work, because a person needs employment from the learning. The companion paper treats employer adoption, hiring, and employment as evidence about the person, tracked at three months, six months, and each year after. Each program defines placement before launch.

Why the BOCES model fits

New York already operates the structure this design needs. Barry Tech, the Nassau BOCES career and technical education center, runs half-day programs in morning sessions from 7:50 to 10:20 and afternoon sessions from 11:50 to 2:20. It awards a certificate at the end of a two-year program and charges families no tuition (Nassau BOCES, 2026). The last point matters to the access test, because a program that charged families would reproduce the divide AI already threatens to deepen.

A NYSED-approved career and technical education program needs at least three units of program-specific content and half a unit of Career and Financial Management. It must also offer work-based learning and a technical assessment (New York State Education Department [NYSED], n.d.-f). The assessment has three parts: an industry-developed written component, an industry-developed performance component, and a locally developed student project (NYSED, n.d.-h). Only public education agencies accredited as providers of secondary education may apply for approval (NYSED, n.d.-c). A BOCES or school district would therefore hold the program and answer for it.

Structure

The 4.0-unit figure follows the Barry Tech model, where two years of approved study carry 4.0 units (Nassau BOCES, 2026). A district may apply some of those units as integrated academic credit at its discretion.

The daily block, 150 minutes

The studio is where the day is self-directed. Students move through project tracks at their own pace, on district-approved tools, inside a protected environment, with every checkpoint visible to the teacher. Routine work closes at the learner checkpoint, while milestones, graded work, and escalations close at the instructor checkpoint. Ten minutes therefore holds real review rather than a stack of signatures. The program assigns no required homework because its friction belongs inside the supervised block, where a named human can see whether the friction is working.

Grade 11: the shared foundation, 450 hours

Grade 11 is the foundation half, and every student takes all of it. The year opens with Ethical AI: The Ground Rules, and no assignment uses AI until the class is complete. The teacher supplies the checkpoint templates, the role assignments, and the approved sources. Students work on a single platform during the opening unit and then add external sources and a second distinct AI system to the challenge step. Disagreement between systems becomes visible early, and the student learns to arbitrate it. Every project begins as a Factics chain with its measure written before the work starts.

Grade 12: one track, core and electives, 450 hours

Grade 12 is the track half. Each student completes one track’s core and chooses the rest of the year from that track’s electives. A BOCES could approve the three tracks as pathways within one program or as separate programs, as the state process allows.

Track 1 students learn to direct AI, evaluate its output, and set policy. They write an AI use policy for a real organization, place its checkpoints, and apply the fifteen transformation requirements at the scale of a team. They also judge tools and vendors by the checkpoint measures rather than by sales presentations (Puglisi, 2026f). Their capstone is a Governed AI Implementation Plan for the internship site, with all six roadmap components.

Track 2 students learn to architect governance into systems. The agents core teaches what an agent is: a model given a goal, credentials, tools, memory, and the autonomy to chain actions across many steps (Cycode, 2026). Track 2 students then build a small agent inside a sandbox that carries only the tools and permissions its task requires and logs every action.

The track sits inside an existing career and technical education tradition, since Barry Tech already runs Teacher Aide Preparation among its courses (Nassau BOCES, 2026). Track 3 students learn to teach the method rather than the tool. They run Checkpoint Sessions for younger students and peers, place the Human First segment where it protects each learner’s own idea, and withdraw their scaffolding on a schedule. They qualify every statistic they teach with before they teach it.

In every track the second year hands the checkpoints to the students, who write their own. The exit target for grade 12 is the Professional rung (Puglisi, 2026e). A student at that rung requires arguments for and against, verifies that sources resolve and support the claim, and demands gap disclosure rather than fabrication. The student then checks that it happened.

Work-based learning

The grade 11 hours would run as industry-based projects, in which an employer brings a real question to the studio and returns to review the student work. Short job shadowing visits would round out those hours (NYSED, n.d.-i). The grade 12 internship would run in the spring semester, two block days a week for 18 weeks, at a supervised site.

Each intern’s checkpoint log would travel to the site supervisor, who holds the designated workplace checkpoints for the placement while the BOCES keeps oversight of the learning program. The 120 total hours exceed the 54 hours of work-based learning that the state attaches to the CDOS credential option (NYSED, n.d.-h).

Assessment, credentials, and measurement

The program’s own defense would sit inside the locally developed project component of the state assessment, and it would run in two parts. In the tool-removed part, the student works without AI. The student explains each checkpoint decision in the capstone and locates errors the assessor has seeded into a copy of the student’s own work. The student then solves a related problem not seen before. In the governed part, the student applies the full Checkpoint Session to a new problem with the tools the student would use at work. The first part shows whether the capability lives in the student, and the second shows whether governance holds with the tools present. HEQ distinguishes demonstrated practice from described practice and gives described practice only partial credit, and the defense scores the same way (Puglisi, 2026e).

Students who complete the full program would earn the technical endorsement on the diploma once the program holds NYSED approval (NYSED, n.d.-h). Students who complete 216 hours of coursework, including at least 54 hours of work-based learning, may earn the CDOS credential even without completing every program requirement. That route also requires a career plan, an employability profile, and the required CDOS learning standards (NYSED, n.d.-h). Industry credentials from Microsoft, Google, and IBM would embed as stackable proof alongside the portfolio.

HEQ would serve as the pre-program measurement. Before the first block, each student would take the offline HEQ exam, a paced ninety-minute set of practical AI-use scenarios. A named evaluator converts the results into a report card and an audit record (Puglisi, 2026e). That reading sets the baseline against which growth is measured rather than deciding who enrolls. HEQ would run again at the close of each semester and upon completion. Its results would count as one part of the student’s evaluation alongside the checkpoint logs, the tool-removed checks, the coursework, and the defense.

Path to approval

NYSED approves new programs through an annual application cycle, and an approved program holds approval for five years, renewed through self-study and external review (NYSED, n.d.-a; NYSED, n.d.-e). The amendment process does not offer a shortcut for a design of this scope. Amendments are for minor changes that do not substantially affect the overall program (NYSED, n.d.-e). Changes to CIP code, CTE content, or academic content are substantial and may require a new approval with its own self-study and external review.

Why an Associate in Applied Science

The design uses the Associate in Applied Science. In New York an AAS requires one third of its content in the liberal arts and sciences, a minimum of 20 credits (NYSED, n.d.-d). The Associate in Occupational Studies includes no liberal arts and sciences courses at all (Rochester Institute of Technology, n.d.). The AAS preserves a liberal arts component and can be built with articulation agreements that create transfer pathways while keeping its occupational preparation. Transfer depends on those agreements rather than on the degree type alone. Its liberal arts slots also carry the human side of the Augmented Intelligence definition: logic, statistics, ethics, judgment, writing, speaking, and the economics of work.

Year 1: the foundation, all three tracks

The first year and the summer placement carry the foundation, 32 credits that every student takes. The second year carries the track, 32 credits of track core and electives, and the degree totals 64 credits.

Track 1 electives: AIS 270 AI in Operations and Workflow Redesign; AIS 275 AI Content, Media, and Disclosed AI-Generated Work; AIS 280 Configuring Agents for Operations; AIS 285 Product and Change Management with AI; any Track 2 course with the program’s approval.

Track 2 electives: CSC 250 Production AI: Cloud, Security, and Deployment; AIS 245 Multi-AI Orchestration Engineering; AIS 255 Agent Security and Threat Modeling; AIS 265 Robotics and Embodied AI Safety; any Track 1 course with the program’s approval.

Track 3 electives: EDU 240 Workshop and Microlearning Design; EDU 250 AI, Accessibility, and Assistive Use; EDU 260 Workplace and Community AI Literacy: Train-the-Trainer; AIS 275 AI Content, Media, and Disclosed AI-Generated Work; any Track 1 or Track 2 course with the program’s approval.

The Minds seminars, HUM 101 through HUM 107, are one-credit electives, one class for each of the seven parts of the strand, open to all three tracks. Each track carries four liberal arts courses, so every track reaches 21 liberal arts credits without depending on how the college classifies the seminars.

The foundation courses carry the author’s methods to every student. AIS 100 opens the degree and the minor with the ground rules, before any course uses AI. AIS 101 teaches the Checkpoint Session with the challenge prompt set. AIS 110 teaches Factics as the discipline of evidence, with the four distinctions between capability and value, output and evidence, recommendation and decision, and completion and success.

The Augmented Intelligence Core as certificate and minor

The six foundation AIS courses, AIS 100 through AIS 140, form an 18-credit Augmented Intelligence Core. Within the associate degree the core would also award an embedded certificate, following the stackable model other colleges have begun to use. At a four-year institution the core would anchor a 21-credit minor open to any major, with one track course added. That course would be AIS 250 for a leadership focus, AIS 240 for a technical focus, or EDU 210 for a teaching focus.

Measurement before, during, and at completion

HEQ would serve as the pre-program measurement for the degree and for the minor. The college may use that reading as one part of admission into either, alongside the application. HEQ would run again at the close of each semester and upon completion, and each semester would also close with a tool-removed check on retention and transfer. Semester results would enter the student’s progress review for both the degree and the Augmented Intelligence Core. A named faculty reviewer would weigh them against the checkpoint logs and course grades.

Hours and sessions

New York defines a semester hour as at least 15 hours of instruction and 30 hours of supplementary assignments. A typical three-credit course therefore carries 45 hours in class and 90 hours of outside work (New York State Higher Education Services Corporation, n.d.). About two thirds of the learner’s time in the degree is self-directed by design of the credit itself. The program treats that time as governed practice rather than unstructured homework. Three-credit courses would meet in two 90-minute sessions a week, and the four-credit programming courses would add a weekly lab. A full-time student would carry about 15 scheduled hours and 30 outside hours in a typical week. Every outside assignment that uses AI would carry the same checkpoint log the classroom uses, reviewed by the instructor as the named human.

The career tie-in

P-TECH supplies the tie-in model: it opened in Brooklyn in 2011 as a partnership of four institutions. The partners were the New York City Department of Education, the City University of New York, the New York City College of Technology, and IBM. Students worked toward a cost-free associate degree with professional mentors, workplace experiences, and internships, and graduates were to stand first in line for entry-level openings at IBM (MDRC, n.d.). This degree would adopt the same structure through an employer consortium whose members commit four things. They would supply real problems for studios and capstones, mentors, the paid summer placement and the second-year apprenticeship practicum, and first-in-line interviews for graduates. The consortium would commit to interviews rather than guaranteed hires, and the college would advertise only what its partners have signed.

Path to approval

Every associate degree needs at least 60 semester hours, and a college must register the curriculum before it publicizes the program, recruits, or enrolls students (NYSED, 2018).

The degree becomes the domain

A graduate or professional already brings disciplinary knowledge and context formed in a field. The Augmented Intelligence definition assigns the governing structure to the human. Governing structure comes from knowing the domain well enough to frame the problem, designate the authoritative sources, and recognize a wrong answer that sounds right. The program would therefore treat each learner’s degree as the domain of specialization.

The foundation terms therefore run in the learner’s own field from the first week. Every learner practices the same segment of the Checkpoint Session in the same week. The nurse works on clinical claims, the attorney on authorities and propositions, the accountant on financial assumptions, the teacher on learning content, and the engineer on technical claims. The standard stays common while the domain stays the learner’s.

Calendar and weekly rhythm

Terms 1 to 3 carry the foundation, Terms 4 and 5 the track, and Term 6 the capstone. A one-week break follows each of the first five terms, and week 78 holds the defense panels and completion.

Eight hours a week fits a working professional’s calendar, and the design accepts what that load costs. The guided self-directed hours carry structure: each week names the problem, the designated sources, the Factics measure, and the checkpoint the learner must close before the next seminar. Self-direction here means pacing and ownership rather than an unguided reading list.

Terms

The first three terms carry the foundation and the last three carry the track, so the professional program splits in half the same way the other two do.

In the foundation terms the weekly Minds seminar carries the survey of all seven parts. In the track terms the same hour becomes an elective, and each learner chooses one deep-dive class per term from the seven.

Three tracks

The first two tracks map to the roles SignalFire (2026) found growing while overall new graduate hiring contracted. Track 1, Professional and Leadership, prepares the people who direct AI, evaluate automated output, and set policy. They include the evaluation designers and product builders the report urges employers to hire. Its concentrations let a nurse focus on clinical assurance and a manager focus on workflow redesign without taking the same courses. Track 2, Technical and Engineering, prepares forward-deployed and solutions engineers who help organizations adopt and integrate AI. Its electives let a learner go deeper on orchestration, security, deployment, or embodied systems. Track 3, Teaching and Enablement, prepares the enabling roles the federal framework names: trainers and teachers, managers who lead team adoption, career counselors, and peer champions (U.S. Department of Labor, 2026). It fits the degree holder who already teaches, trains, supervises, or advises. It gives that person a governed method to teach rather than a set of tools to present. A learner who finishes one track can return for another without repeating the foundation.

Exit standard and capstone

The program’s exit target is the Expert rung, where a learner brings a validated method and enforces conformance to it, at Method Governance Level 4. All three capstones ask for Thought Leader behavior. The Track 1 and Track 2 capstones work inside one workflow at the partner employer. Their checkpoints carry named authority over both the process and the decision (Puglisi, 2026f). The Track 1 learner applies the fifteen requirements of What AI Transformation Actually Requires and delivers a Governed AI Implementation Plan with the six roadmap components (Puglisi, 2025). The Track 2 learner builds a Governed System with the five roadmap components and runs it on the workflow. The Track 1 and Track 2 learners report the checkpoint measures on the result. The first is the reversal rate at checkpoints, and the second is the rate at which sampled checkpoint decisions agree with independent expert review. The third is the success rate in reconstructing a consequential decision from the record. The Track 3 learner delivers a Governed AI Learning Design to a real cohort and measures whether the cohort learned, including performance with the tools removed. The learner also reports the checkpoint measures from the cohort’s own logs. A graduate leaves able to govern AI in the field of the original degree. The graduate can hand the partner a working policy, a working tool, or a working course.

Delivery routes

A college partner could offer the certificate through continuing education or as a registered certificate. An independent provider in New York would face licensing, because the state requires non-degree proprietary schools to be licensed unless they meet an exemption. An initial license runs two years, and the state’s Bureau of Proprietary School Supervision licenses, monitors, and regulates private career schools (NYSED, n.d.-b; Office of the New York State Comptroller, 2023).

Financing this certificate would rest on employers, tuition, or institutional funds.

Entering the current system

Where implementation could fail

• Approval is slow, and staffing may be slower. A BOCES program waits on the state’s annual cycle and may need a new approval rather than an amendment. An associate degree waits on campus governance and state registration, and an independent certificate waits on licensing.
• The associate degree carries 64 credits, four above the state floor, and its final semester holds both the practicum and 13 credits of coursework. Every added credit costs time and money and weighs on completion, so a partner college may press the design toward 60 or 62 credits.
• The professional certificate trades depth for access, because eight hours a week over 18 months fits a working life. It covers the technical fundamentals a practitioner who governs AI needs rather than the depth an engineer needs. Track 2 learners without programming experience would carry the heaviest load.
• Work-based learning depends on sites. Internships, practicums, and employer-embedded capstones each need a supervisor willing to act as a second named human. A region without enough employer partners would struggle to place every learner.
• Choice leaves gaps by design, since a Track 1 graduate may never build an agent from code. A Track 2 graduate may never write an organization’s AI policy, and a Track 3 graduate may do neither.

What a partner decides

These designs are offered as starting points for a partner institution rather than finished catalogs. The partner confirms the occupational mapping and the approval route with the state and sets the credit total within the state’s rules. It also chooses which electives it can staff in its first year and names the employers who will bring real problems and host learners. It names the humans who will sign at each checkpoint tier as well. The standard beneath all three programs stays fixed while the courses change. That is the rule the author’s learning roadmaps set: the courses are examples, and the structure is constant. A partner may also lower costs by partnering with companies and drawing on existing courses, such as industry credentials and openly licensed course materials. Each one would still run inside the Checkpoint Session and its checkpoint log.

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