Reusable AI capability
Document Responsible AI Use in Coursework
Maintain a proportionate, honest record of material AI inputs, decisions, source checks, revisions and learning reflection without declaring authorship or policy compliance.
This Skill is a reusable set of instructions based on the linked Amo.ng Prompt or Workflow. It tells you what to provide, how to apply the method and how to know when the task is complete. It does not run automatically.
# Document Responsible AI Use in Coursework Skill ID: AMO-S-000034 Skill URL: https://amo.ng/skills/document-responsible-ai-use-coursework Purpose: Document how AI materially influenced coursework so the learner can explain what was supplied, accepted, rejected, checked, changed and learned across repeated tasks. Required inputs: - Applicable course or institutional AI-use and disclosure rules. - Material prompts or summarized AI interactions with dates and tool/model details where known. - Learner decisions, source checks, accepted/rejected content and revisions. - The learner’s own explanation of what changed in their reasoning or skill. How to use: When to use: - While completing permitted AI-assisted coursework or a supervised learning exercise. - When a course asks for an AI-use disclosure, process log or reflective account. - When a learner wants evidence of decisions and source verification rather than a raw transcript dump. When not to use: - To certify authorship, originality, academic integrity or institutional compliance. - To reconstruct an interaction from memory after the fact while presenting it as contemporaneous evidence. - To store private, sensitive, copyrighted or assessment-restricted material in an unauthorized tool. Instructions: Use the source Prompt throughout the work rather than only at submission. 1. Record only material AI interactions and minimize assessment content or personal data. 2. For each interaction, distinguish the learner input, system output, decision, verification, revision and learning evidence. 3. Link consequential factual claims to sources the learner actually opened and checked. 4. Preserve rejected suggestions and the reason where they materially affected the work. 5. Map the record to the supplied disclosure rule while marking ambiguous or missing policy for the lecturer or policy owner. 6. Produce a concise disclosure draft and learning reflection that the learner reviews and owns. The record supports transparency; it is not automatic proof of authorship, misconduct, learning or compliance. Expected output: A chronological AI-use ledger, claim/source check record, accepted-and-rejected decision trail, revision map, learner-authored reflection prompts, disclosure draft and unresolved-policy note. Constraints and boundaries: - Do not invent past interactions, dates, source checks or learning evidence. - Do not retain unnecessary personal, peer, assessment or copyrighted content. - Do not determine authorship, misconduct, grades or institutional compliance. - The learner owns the final disclosure; the lecturer or policy owner interprets institutional rules. Based on Prompt: AI-Assisted Coursework Provenance and Learning Reflection Record Source ID: AMO-P-000327 https://amo.ng/prompts/ai-assisted-coursework-provenance-learning-reflection-record Completion criteria: Complete when all material AI interactions are represented; accepted and rejected outputs are distinguishable; consequential claims have actual source-check status; revisions and learning are explained by the learner; applicable disclosure fields are addressed; and missing policy or evidence remains explicit. Use this Amo.ng Skill with your preferred AI tool. Supply the required inputs and follow the usage instructions. # Document Responsible AI Use in Coursework Skill ID: AMO-S-000034 Skill URL: https://amo.ng/skills/document-responsible-ai-use-coursework Purpose: Document how AI materially influenced coursework so the learner can explain what was supplied, accepted, rejected, checked, changed and learned across repeated tasks. Required inputs: - Applicable course or institutional AI-use and disclosure rules. - Material prompts or summarized AI interactions with dates and tool/model details where known. - Learner decisions, source checks, accepted/rejected content and revisions. - The learner’s own explanation of what changed in their reasoning or skill. How to use: When to use: - While completing permitted AI-assisted coursework or a supervised learning exercise. - When a course asks for an AI-use disclosure, process log or reflective account. - When a learner wants evidence of decisions and source verification rather than a raw transcript dump. When not to use: - To certify authorship, originality, academic integrity or institutional compliance. - To reconstruct an interaction from memory after the fact while presenting it as contemporaneous evidence. - To store private, sensitive, copyrighted or assessment-restricted material in an unauthorized tool. Instructions: Use the source Prompt throughout the work rather than only at submission. 1. Record only material AI interactions and minimize assessment content or personal data. 2. For each interaction, distinguish the learner input, system output, decision, verification, revision and learning evidence. 3. Link consequential factual claims to sources the learner actually opened and checked. 4. Preserve rejected suggestions and the reason where they materially affected the work. 5. Map the record to the supplied disclosure rule while marking ambiguous or missing policy for the lecturer or policy owner. 6. Produce a concise disclosure draft and learning reflection that the learner reviews and owns. The record supports transparency; it is not automatic proof of authorship, misconduct, learning or compliance. Expected output: A chronological AI-use ledger, claim/source check record, accepted-and-rejected decision trail, revision map, learner-authored reflection prompts, disclosure draft and unresolved-policy note. Constraints and boundaries: - Do not invent past interactions, dates, source checks or learning evidence. - Do not retain unnecessary personal, peer, assessment or copyrighted content. - Do not determine authorship, misconduct, grades or institutional compliance. - The learner owns the final disclosure; the lecturer or policy owner interprets institutional rules. Based on Prompt: AI-Assisted Coursework Provenance and Learning Reflection Record Source ID: AMO-P-000327 https://amo.ng/prompts/ai-assisted-coursework-provenance-learning-reflection-record Completion criteria: Complete when all material AI interactions are represented; accepted and rejected outputs are distinguishable; consequential claims have actual source-check status; revisions and learning are explained by the learner; applicable disclosure fields are addressed; and missing policy or evidence remains explicit.Copy Skill copies the full Skill instructions. Use with AI copies those instructions with a short guide for your chosen AI tool. Both actions only copy text—they do not send anything to an AI provider or run the Skill.
Portable Agent Skill
Instruction-only package. Inspect the contents and your agent client’s permissions before use.
Purpose
Document how AI materially influenced coursework so the learner can explain what was supplied, accepted, rejected, checked, changed and learned across repeated tasks.
Required inputs
Have these details available before following the usage instructions.
- Applicable course or institutional AI-use and disclosure rules.
- Material prompts or summarized AI interactions with dates and tool/model details where known.
- Learner decisions, source checks, accepted/rejected content and revisions.
- The learner’s own explanation of what changed in their reasoning or skill.
How to use this Skill
When to use:
- While completing permitted AI-assisted coursework or a supervised learning exercise.
- When a course asks for an AI-use disclosure, process log or reflective account.
- When a learner wants evidence of decisions and source verification rather than a raw transcript dump.
When not to use:
- To certify authorship, originality, academic integrity or institutional compliance.
- To reconstruct an interaction from memory after the fact while presenting it as contemporaneous evidence.
- To store private, sensitive, copyrighted or assessment-restricted material in an unauthorized tool.
Instructions:
Use the source Prompt throughout the work rather than only at submission.
1. Record only material AI interactions and minimize assessment content or personal data.
2. For each interaction, distinguish the learner input, system output, decision, verification, revision and learning evidence.
3. Link consequential factual claims to sources the learner actually opened and checked.
4. Preserve rejected suggestions and the reason where they materially affected the work.
5. Map the record to the supplied disclosure rule while marking ambiguous or missing policy for the lecturer or policy owner.
6. Produce a concise disclosure draft and learning reflection that the learner reviews and owns.
The record supports transparency; it is not automatic proof of authorship, misconduct, learning or compliance.
Expected output:
A chronological AI-use ledger, claim/source check record, accepted-and-rejected decision trail, revision map, learner-authored reflection prompts, disclosure draft and unresolved-policy note.
Constraints and boundaries:
- Do not invent past interactions, dates, source checks or learning evidence.
- Do not retain unnecessary personal, peer, assessment or copyrighted content.
- Do not determine authorship, misconduct, grades or institutional compliance.
- The learner owns the final disclosure; the lecturer or policy owner interprets institutional rules.
Based on an Amo.ng Prompt
AI-Assisted Coursework Provenance and Learning Reflection Record
Open the linked Prompt to read the instructions this Skill is based on.
Completion criteria
Complete when all material AI interactions are represented; accepted and rejected outputs are distinguishable; consequential claims have actual source-check status; revisions and learning are explained by the learner; applicable disclosure fields are addressed; and missing policy or evidence remains explicit.
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