Local Policy

Local Policy Development and Practice

State guidance and national frameworks establish direction, but meaningful artificial intelligence expectations are ultimately defined within schools, districts, and classrooms. Local policies translate broad principles into daily instructional practice by clarifying when AI may be used, how it may be used, and what responsibilities remain with the learner.

At the ground level, effective policy rarely begins with complex governance structures. Instead, it often starts with a simple instructional question:

Is AI permitted in this learning environment, and if so, to what degree?

Answering this question transparently helps reduce confusion, supports academic integrity, and builds student capacity to use AI responsibly. Many institutions are moving away from binary allowed/prohibited models and toward signaling frameworks that describe degrees of acceptable use aligned with learning goals. 

Examples from Local Institutions

One widely adopted approach is the use of visual or categorical indicators that communicate expectations clearly for each assignment or learning context.  In The Citadel's Generative AI Course Policy, teachers are required to adopt an AI policy and use traffic lights as guides.

This model provides instructional flexibility while ensuring students receive consistent messaging about expectations. Similar scaled approaches appear in broader guidance tools, such as Oklahoma's assignment rating scale and Washington's scaffolding scale, reinforcing the value of clearly signaling degrees of AI involvement rather than relying on binary permission structures. 

Greenville County Schools communicates expectations through a philosophical position rather than assignment-level signaling. Their guidance emphasizes that AI should augment instruction rather than replace human interaction, while reinforcing: 

• alignment with pedagogical goals

• protection of student privacy

• professional learning support for educators

• ongoing evaluation of instructional impact
  
  

Together, these examples illustrate two viable local approaches: structured signaling and guiding philosophy. Many systems employ a combination of both.

Planning Development Supports

In addition to signaling expectations for AI use, educators often benefit from structured tools that help translate instructional intent into clear syllabus or policy language. These supports are designed to simplify drafting rather than prescribe specific positions. 

One example is Stanford University’s AI Syllabus Policy Worksheet, which streamlines the process of creating a course statement on AI use. The worksheet provides selectable snippets of sample language that instructors can combine to produce a first draft of their policy. After assembling their selections, instructors are guided to use a chatbot prompt to generate alternative draft variations, which they then edit and refine before including the final language in their syllabus. 

As a next step, consider using this adapted worksheet for K-12 to draft or refine your own statement on AI use. By selecting sample language, assembling a draft, and iterating with AI-supported revisions, educators can quickly translate instructional intent into clear expectations for students. Whether applied at the course, department, or school level, this process provides a structured pathway for turning reflection into practice.