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Orange County Public Schools Student AI feedback analysis

Student town hall analysis

Artificial intelligence feedback from students, organized for policy and practice.

This site summarizes student feedback from Orange County Public Schools (OCPS) town halls on artificial intelligence (AI). The analysis separates major themes, specific details, tensions, and practical actions that appeared across the student comments.

Dates: April 21-May 1, 2026 School sections: 6
6School sections: Boone, Evans, Dr. Phillips, Ocoee, Timber Creek and Horizon.
8Major themes carried enough detail to support district-level analysis.
7Specific proof ideas surfaced before grade or discipline consequences.
5Design conditions students said would determine whether a district AI tool is used.

Charts and signal map

Where the feedback is strongest

The charts use coded feedback signals from the document. They should be read as qualitative evidence strength, not as a survey or vote count.

The strongest pattern is not pro-AI or anti-AI. It is a request for shared expectations, teacher AI literacy, due process, and human judgment.

Theme Strength

Relative coding score based on school coverage, recurrence, and specificity.

Proof Before Consequence

How many school sections raised each proof idea.

School-by-Theme Heat Map

Darker cells show themes that were more developed in that school section.

 
Not observed Present Recurring Major theme
Major theme: Strongly developed across that school's comments.
Recurring: Mentioned more than once or with clear detail.
Present: Appeared, but with limited detail.
Not observed: Not found in that school section.

Major themes

Eight feedback areas students returned to repeatedly

Select a theme to review its analysis, specific student details, and implications for policy or implementation.

Several themes overlap. For example, students linked teacher AI grading to fairness, classroom relationships, proof requirements, and assignment design.
 
 

Due process pattern

A student-centered protocol emerged from the comments

Students did not reject accountability. They rejected weak evidence. The strongest student preference was a process that combines teacher judgment, student explanation, and artifact review.

A detector flag can start a review, but students consistently said it should not end the review.
1Clarify the assignment ruleMake AI expectations visible before work begins, including what support is allowed.
2Review the work historyCheck drafting patterns, edit history, source use, and signs of copy-paste.
3Compare prior workUse baseline samples and earlier assignments to understand normal student voice and growth.
4Talk with the studentAsk the student to explain the reasoning, process, sources, and content understanding.
5Decide proportionallyUse more than one signal before a zero or discipline consequence, and match the consequence to the violation.

Interactive boundary explorer

How students described the line between support and misconduct

Click a scenario to see how it maps to the feedback. This is not a final policy definition. It is a way to translate student language into decision points.

Students tended to accept AI when it preserved student thinking and objected when AI replaced student thinking.
 
 

Evidence explorer

Specific details from the student feedback

Use the filters to review paraphrased feedback points by school and theme. The entries preserve student meaning while cleaning transcript fragments for readability.

Search terms such as "detector," "LaunchPad," "mental health," "writing," or "homework" will narrow the evidence quickly.
 
 

Analysis to action

Implementation moves supported by the feedback

These actions are written as practical design moves rather than final policy language. They translate the major themes into work the district can test, refine, and communicate.

The center of gravity is shared clarity: the same district baseline, with assignment-level guidance that teachers and students can actually apply.
 
OCPS student AI feedback analysis