How to Turn Course Feedback and Lesson Analytics Into a Repurposing Priority Map With Gemini

Problem statement and who this is for

This workflow is for course creators, coaches, and educators who already have more content than time. The question is not what you could repurpose. The question is what you should repurpose first.

The most reliable answer usually sits in two places: lesson analytics and learner feedback. When those two are combined, you can see which lesson is confusing, which idea gets replayed, which question keeps coming up, and which topic deserves a lighter-weight asset such as a worksheet, FAQ, blog post, or starter guide.

Prerequisites

• A Gemini account
• A spreadsheet export of course or lesson analytics, or a manually built sheet with one row per lesson
• Feedback data from surveys, email replies, comments, or support questions
• Permission to use the feedback after removing names and personal details
• A list of the repurposing formats you actually use, such as blog posts, worksheets, audio summaries, FAQs, or lead magnets

How to capture or gather the source material

Export lesson analytics into a spreadsheet or comma-separated values file. Keep one row per lesson or module. Useful columns include lesson title, completion rate, average watch time, replay notes, drop-off notes, question count, and any manual observations you track.

Put learner feedback into a second tab or a second file. You do not need advanced data infrastructure. A cleaned spreadsheet with lesson name, comment text, and optional sentiment or issue type is enough.

Remove names and direct identifiers. Then add one simple reference column that matches the feedback to the lesson or module when possible. If the feedback is too messy for that, group it by broad topic first.

Step-by-step workflow

1. Clean the spreadsheet so each lesson has a stable name and each feedback comment is readable.
2. Upload the spreadsheet and any feedback document to Gemini.
3. Ask Gemini to identify recurring friction points, high-interest topics, and lessons that generate repeated questions or unusual replay behavior.
4. Ask for a priority map that recommends the next repurposed asset for each strong candidate lesson. Good examples are FAQ, worksheet, short guide, blog post, or audio summary.
5. Review the ranking with common sense. A loud complaint is not always the best content opportunity. A repeated beginner question often is.
6. Pick one or two assets to build first and save the rest of the list as your next content queue.

Tool-specific instructions

Primary tool: Gemini

Gemini is the best primary path here because it can analyze uploaded files, including spreadsheets, and it fits a Google-centered workflow well. Keep the data tidy enough that lesson names and comments are readable, then ask for ranking and repurposing recommendations in one pass.

Alternative: ChatGPT

ChatGPT is a good fallback when you want fast spreadsheet interpretation and a sharper content brief after the ranking is done. It works well if your feedback is already in a readable exported file.

Alternative: Claude

Claude is strong when the feedback is long, qualitative, and messy. It can help cluster themes and rewrite the final priority memo in clearer language.

Alternative: NotebookLM

NotebookLM is a useful secondary layer when the source base includes feedback documents, meeting notes, transcripts, or broader course material, not just a spreadsheet. It is less direct than Gemini for the spreadsheet-first version of this workflow.

Copy and paste prompt blocks

Gemini analysis prompt

{
  "task": "Analyze the uploaded analytics and feedback sources to identify the best repurposing opportunities.",
  "instructions": [
    "Use the spreadsheet and feedback sources together.",
    "Identify lessons or topics that show repeated questions, confusion, strong interest, or unusual replay behavior.",
    "Recommend the best next asset type for each strong candidate, such as FAQ, worksheet, blog post, lead magnet, or audio summary.",
    "Base the ranking on evidence from the uploaded data.",
    "Flag weak or ambiguous cases instead of overstating them."
  ],
  "output_format": {
    "section_1": "Top repurposing opportunities ranked",
    "section_2": "Evidence used for each ranking",
    "section_3": "Best asset type for each lesson or topic",
    "section_4": "Fastest wins versus bigger strategic opportunities"
  }
}

Priority map prompt for any chat app

{
  "role": "content operations analyst",
  "goal": "Turn the analysis into a practical repurposing queue.",
  "instructions": [
    "Recommend only asset formats that I can realistically create.",
    "Keep the list short and ranked.",
    "Include why each item matters and what source evidence supports it.",
    "Do not confuse low completion with low value if the feedback shows strong interest."
  ],
  "output_format": {
    "table_columns": ["priority", "lesson_or_topic", "recommended_asset", "why_now", "evidence", "estimated_effort", "next_action"],
    "final_section": "What to deprioritize for now"
  }
}

Quality checks

• The ranking should point to clear evidence in the data.
• Beginner confusion and repeated questions should usually outrank vague complaints.
• The recommended asset type should match the problem. For example, repeated confusion might need a FAQ or worksheet, while a high-interest topic might deserve a lead magnet or article.
• The final queue should be short enough to act on.

Common failure modes and fixes

• The data is too messy. Clean the lesson names and feedback labels before upload.
• One loud comment dominates the ranking. Ask the model to prioritize repeated patterns, not one-off reactions.
• The output recommends assets you will not actually make. Give the model a fixed list of allowed asset types.
• The priority map is too long. Ask for the top five only, divided into fast wins and strategic opportunities.

Sources Checked

• https://support.google.com/gemini/answer/14903178?co=GENIE.Platform%3DDesktop&hl=en (accessed 2026-03-26)
• https://help.openai.com/en/articles/8555545-file-uploads-faq (accessed 2026-03-26)
• https://help.openai.com/en/articles/9260256-chatgpt-capabilities-overview (accessed 2026-03-26)
• https://support.anthropic.com/en/articles/8241126-what-kinds-of-documents-can-i-upload-to-claude-ai (accessed 2026-03-26)
• https://support.google.com/notebooklm/answer/16206563?hl=en (accessed 2026-03-26)

Quarterly Refresh Flag

Review this article by 2026-06-24.