AI Authorship Prompt Protocol

This protocol supports student reflection and accountability when using AI tools (e.g., ChatGPT, Claude) in the production of any written or coded artifact.

The core question is not: Did you use AI? It’s: How did your use of AI shape your thinking, and can you prove it?

This document outlines the reflection, submission, and self-evaluation process for any AI-assisted work.

🧾 Submission Components

Every AI-assisted artifact must include:

1. Prompt Log

A record of actual prompts used in the process. This is not optional. It must:

  • Include at least 3 prompts
  • Include both “successful” and “useless” attempts
  • Show iteration or escalation (not just one prompt and copy-paste)

Example Format:

Prompt Log: System Diagram Project
1. "How do you explain the difference between a process and a feedback loop in a system?"
2. "List metaphors for systems thinking that a 9th grader might understand."
3. "Rewrite this explanation using student-friendly language."

2. Reflection on Effectiveness

Write 1–2 paragraphs answering:

  • What did the AI get right?
  • What did it miss or mess up?
  • What did you have to bring to the process to make the output meaningful?

3. “Should-Have” Prompt Set (optional but diabolically illuminating)

Based on your final artifact, write 2–3 prompts your future self should have used to generate deeper or clearer thinking. This helps surface gaps between intention and outcome.

Meta-Prompt (you can use this to generate should-haves):

“Look at this artifact. What prompts would have led me to something more precise, insightful, or rigorous?”

🧠 What This Teaches

  • Prompts are evidence of process
  • AI use requires discernment and editing
  • Revision is not just about text—it’s about questions asked

Students learn that writing with AI is not shortcutting—it’s shaping. Prompts are the intellectual fingerprints of their work.

🔍 Instructor Checkpoints

Use the following when reviewing prompt logs:

Signal What It Shows
Multiple attempts Student is iterating, not offloading
Specific prompts Student understands the tool, not just the topic
“Why” reflection Student is noticing bias, vagueness, or overreach
Self-generated “should-haves” Student is building discernment, not faking output

💡 Classroom Use

  • Introduce with your AI Policy Rollout Lesson (Unit 0)
  • Apply to blogs, diagrams, technical docs, and any AI-touched work
  • Use samples (real and fake) to model what strong prompt logs look like
  • Revisit midyear as AI habits evolve