AI Playbooks for Mississippi Work and Learning
Practical use cases, workflows, safeguards, and implementation steps. Read each playbook online as a web guide or download its complete, print-ready PDF.
Choose a practical AI playbook
Schools and colleges
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K–12 Educator AI Playbook
Plan classroom activities, set clear assignment rules, verify lessons, protect student information, preserve accessibility, and coordinate responsible school and district review.
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Career and Technical Education AI Playbook
Map AI-assisted assignments to occupational competencies, verify student work, assess demonstrated competence and plan a bounded instructional pilot with seven working tools.
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AI + Dual Enrollment Guide
Coordinate AI expectations for a college course taught in a K–12 setting, including permissions, tools, student data, access, academic authority, and responsible learning.
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Faculty AI Playbook
Set clear course rules, design learning-centered assessments, protect student information, review AI-assisted work, and keep faculty judgment accountable.
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Student AI Playbook
Understand course rules, use AI to support learning, protect personal information, verify outputs, disclose assistance, and submit work responsibly.
Workplaces and organizations
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Manufacturing AI Playbook
Choose appropriate use cases, protect information, run a controlled 30-day pilot, review outputs, and measure results across plant and business workflows.
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Cybersecurity AI Playbook
Govern and secure AI systems, bound AI-enabled defense, prepare for AI-enabled threats, validate outputs, preserve incident evidence, and run a controlled security pilot.
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AI Agents Playbook
Define a bounded task, enforce tool permissions, set human review rules, test failure conditions and decide whether an agent pilot should proceed.
Start with the Statewide AI Framework
The Mississippi Statewide AI Framework (August 2026 PDF, 975 KB) is the foundation for every MAIN playbook. It establishes shared priorities and a competency progression across K-12, postsecondary education and the workforce. Each playbook applies selected domains to a particular audience, workflow or sector and retains the Framework’s human-review and evidence boundaries.
Framework → playbook → workflow and tools → local evidence and improvement. Use the Framework to set direction, the relevant playbook to plan and practice, and your organization’s authorized policies to govern implementation. These voluntary MAIN resources do not override law, adopted standards, course requirements or institutional authority.
Every playbook includes an edition-specific crosswalk. Use its practice and evidence examples for local learning and review; they do not certify proficiency, authorize deployment or establish causal impact. Recheck the playbook when the Framework changes.
What is a MAIN AI Playbook?
A MAIN AI Playbook is a voluntary planning, learning, and workforce resource that helps people move from interest to a controlled AI practice or pilot. It does not replace institutional policy or qualified legal, cybersecurity, safety, engineering, compliance, accessibility, or professional review.
Use cases
Understand where AI may help, where additional controls are needed, and where people must retain decision authority.
Workflows and prompts
Follow practical examples that include inputs, prompts, verification steps, and clear limitations.
Safeguards
Protect confidential information, check outputs, evaluate tools, document important uses, and prepare for incidents.
Implementation
Name an owner, choose a bounded pilot, train participants, measure results, and decide whether to continue.
How to use a MAIN AI Playbook
Use the playbook with the people who own the work, the information, the technology, and the consequences. The goal is a measured decision, not AI adoption for its own sake.
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Define the workflow
Describe the current task, owner, inputs, outputs, reviewers, pain points, and consequences of error.
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Assess the risk
Check the data, tool, user, integration, audience, legal obligations, and potential impact before testing.
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Run a bounded pilot
Name an accountable owner, train a small group, use approved information, require review, and document issues.
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Decide with evidence
Compare quality, total time, consistency, usefulness, and risk, then continue, revise, pause, or stop.
Need help adapting a playbook?
MAIN can help organizations think through responsible AI adoption, workforce training, stakeholder engagement, and practical implementation steps.
If your problem is already bounded and has an accountable owner, intended users, a defined data path, and a desired outcome, review Innovation Hub intake.