Mississippi is taking a defining step in AI education. The Mississippi AI Learning Progression Framework, developed by the AI Workforce Readiness Council and hosted by the Mississippi Artificial Intelligence Network (MAIN), provides a structured, stage-by-stage map of the AI skills learners need from kindergarten through senior career leadership.
This is not a curriculum. It is not a mandate. It is a shared reference point — one that makes expectations visible across every transition in a learner’s journey and gives educators, institutions, and employers a common language for AI readiness.
Why Mississippi Needed an AI Learning Progression Framework
AI is already embedded in agriculture, healthcare, manufacturing, emergency management, and public service across the state. Yet until now, there has been no unified document describing what AI competency looks like at each stage of education and career development.
Without that shared understanding, schools build programs in isolation, colleges guess at what students should already know, and employers fill gaps through ad hoc training. The AI learning progression framework changes that by defining clear expectations at every handoff point — from middle school to high school, from community college to university, and from campus to career.
What the Framework Covers: 11 AI Skill Domains
The framework is organized around 11 AI skill domains, each with its own definition, rationale, learner outcomes, and Mississippi-specific examples. These domains span the full range of what it means to use AI well — not just technically, but ethically, safely, and with human judgment intact.
| # | Domain | Core Idea |
|---|---|---|
| 1 | AI Foundations and Conceptual Understanding | Knowing what AI is and is not — and why confident-sounding outputs can still be wrong. |
| 2 | Data Literacy and Data Stewardship | Understanding how data quality, bias, and privacy shape every AI outcome. |
| 3 | Critical Evaluation of AI Outputs | Treating AI-generated content as a draft, not an answer. |
| 4 | Human-AI Interaction | Communicating intent clearly and refining results through iteration. |
| 5 | Algorithmic Thinking and Problem Decomposition | Breaking problems into steps and deciding where AI helps versus where humans must lead. |
| 6 | AI-Enabled Workflow and System Design | Building processes with human-in-the-loop oversight, including agentic workflows. |
| 7 | Ethical Reasoning and Responsible AI Use | Applying fairness, accountability, transparency, and integrity to every use case. |
| 8 | Human Agency, Judgment, and Restraint | Knowing when not to use AI — and keeping humans accountable for decisions. |
| 9 | Communication and AI Literacy Across Audiences | Explaining AI concepts, risks, and limitations in plain language. |
| 10 | Sector and Pathway Awareness | Connecting AI skills to real career pathways in healthcare, manufacturing, agriculture, and more. |
| 11 | Cyber Security and Safety | Protecting data, recognizing AI-enabled threats, and using AI tools responsibly. |
Four AI Literacy Pillars
To make these domains easier to communicate, the framework groups them under four interconnected AI Literacy Pillars. These pillars give administrators, school boards, and industry partners a quick way to see how the skills connect and where their programs fit.
| Pillar | Domains |
|---|---|
| AI Understanding & Mindset | 1, 5, 8, 9 |
| Responsible Data & Evaluation Practices | 2, 3, 7, 11 |
| Effective Human-AI Collaboration | 4, 6 |
| Contextual Application & Adaptation | 10 + cross-cutting lens |
Eight Developmental Levels, One Continuous Pathway
Each domain is mapped across eight stages: Elementary (K–5), Middle School (6–8), High School (9–12), Community College and Technical Programs, University Freshman–Sophomore, University Junior–Senior, Early Career (0–4 years), and Mid-to-Senior Career and Leadership.
This structure makes it possible to answer practical questions. What should a middle schooler be able to do with data literacy? What does critical evaluation of AI outputs look like for a community college student in a technical credential program? When does cybersecurity awareness shift from recognizing deepfakes to governing enterprise AI risk?
The progression answers each of these — concretely, with action-oriented language tied to Mississippi contexts.
Grounded in Mississippi’s Economy and Communities
One of the framework’s defining features is its commitment to relevance. Every domain includes Mississippi-specific examples drawn from the state’s core sectors: Delta precision agriculture, Gulf Coast resilience, advanced manufacturing and shipbuilding, healthcare, and public service.
A K–12 example might ask whether a pilot-assist system on a Mississippi River towboat qualifies as AI or simple automation. A postsecondary example might involve documenting data lineage for a Gulf Coast shrimp-harvest supply chain. A workforce example might require verifying AI-generated policy recommendations against official coastal resilience dashboards before including them in a report.
These examples are not decorative. They ground the framework in the work Mississippi learners and professionals actually do.
Human Judgment as the North Star
The framework’s guiding conviction is direct: the purpose of AI is not to replace human judgment, but to amplify it.
Domain 8, Human Agency, Judgment, and Restraint, serves as the explicit North Star of the entire document. Every other skill ultimately supports the ability to keep humans in control and make wise decisions about when AI should — or should not — be used.
The framework also includes a set of High-Stakes Human-Review Triggers that apply across all domains and levels. Any AI output that affects grades, financial decisions, safety, employment actions, legal or regulatory content, or sensitive personal data requires mandatory human review before action is taken.
Aligned With National and International Standards
The framework draws on and aligns with leading authorities in AI education, risk management, and workforce readiness, including:
| Authority | Relevance |
|---|---|
| U.S. Department of Labor AI Literacy Framework (2026) | Workforce readiness benchmarks and education-to-career alignment |
| NIST AI Risk Management Framework | Risk-based language on bias, transparency, accountability, and human oversight |
| OECD Digital Education Outlook 2026 | Human-centered design and durable skill emphasis |
| UNESCO Guidance for Generative AI in Education | Disclosure norms, appropriate use, and learner rights protections |
| EU Artificial Intelligence Act | High-risk obligations, transparency rules, and AI literacy requirements |
| ETS K-12 AI Literacy Progression Models | Grade-band progression design and foundational stage structure |
This alignment ensures that Mississippi’s approach is not only locally grounded but globally informed.
A Living Document Built Through Collaboration
The AI Learning Progression Framework was developed by the Curriculum Framework Team, a working group of the AI Workforce Readiness Council established through the Mississippi AI Talent Accelerator Program (MAI-TAP). The Council brings together representatives from more than 20 organizations, including AccelerateMS, Amazon Web Services, NVIDIA, the Mississippi Department of Education, the Institutions of Higher Learning, the Mississippi Community College Board, and universities and colleges across the state.
Because AI is advancing rapidly, this framework is designed to be revisited, updated, and refined as the field evolves.
What This Framework Makes Possible
The AI learning progression framework does not tell any school or employer what to teach or how to teach it. What it does is far more foundational: it defines the shared expectations that allow independent programs to build toward the same outcomes.
With this document in hand, a community college can see what high school graduates should bring to the table. A university can identify where its curriculum reinforces — or leaves gaps in — the progression. An employer can articulate what “AI-ready” means for a new hire in healthcare, manufacturing, or public service.
Mississippi is building something that matters — not by chasing trends, but by investing in the durable skills and shared alignment that make AI work for people.
Mississippi is building something that matters — not by chasing trends, but by investing in the durable skills and shared alignment that make AI work for people.
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The Mississippi AI Learning Progression Framework is a publication of the AI Workforce Readiness Council, produced and hosted by the Mississippi Artificial Intelligence Network (MAIN). To learn more, visit mainms.org.