AI Human Subjects Recruitment Equity Narrative: Drafting Inclusion-Plan Summaries

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-research-AI-and-human-subjects-recruitment-narrative-r8a3-creators

1. A researcher asks an AI tool to draft an inclusion-plan summary from their raw notes on recruitment equity. What should the AI produce?

   1. A final funding proposal ready for submission
   2. A list of all community organizations that could participate
   3. A decision-ready summary with headline, caveats, and explicit unresolved questions
   4. A polished press release suitable for public distribution

2. An AI-drafted inclusion plan names three specific communities to target for recruitment but does not mention any budget for community organizations. What does this indicate about the draft?

   1. The draft is complete and ready for review
   2. The AI made an error and should be rerun
   3. The draft likely fails because it doesn't tie people to dollars
   4. The communities will likely participate without funding

3. Which of the following is something AI cannot do when drafting recruitment equity narratives?

   1. Restructure raw notes into coherent prose
   2. Determine which stakeholders need separate conversations before the document lands
   3. Identify gaps or unresolved questions in the inputs
   4. Surface barriers to representation that appear in the input data

4. Why should an AI-drafted equity narrative include explicit questions or decisions for the reviewer to resolve?

   1. To make the document longer and appear more thorough
   2. Because AI should surface what it cannot decide rather than making assumptions
   3. To shift work back to the researcher inappropriately
   4. To ensure the document meets page requirements

5. What does it mean for an AI to 'read the room' in the context of recruitment equity drafting?

   1. To write persuasive language that encourages participation
   2. To recognize when concerns are political, ethical, or relational rather than analytical
   3. To analyze demographic data from past recruitment efforts
   4. To match recruitment language to the target audience's literacy level

6. A funder requires an inclusion-plan summary for a human subjects research project. Which component is most essential to include?

   1. A budget spreadsheet showing all expenses
   2. Representation goals tied to specific outreach channels and barrier analysis
   3. Detailed biographies of all research team members
   4. A literature review of previous recruitment studies

7. When an AI drafts an inclusion plan, what does the phrase 'tie people to dollars' mean in practice?

   1. Hire a financial manager for the project
   2. Create a cost breakdown of all research activities
   3. Budget for community partners who will do recruitment work, in the same paragraph that names communities
   4. Pay participants directly for their time

8. A researcher receives an AI-drafted recruitment equity narrative. The draft includes a paragraph naming communities to recruit, but the next paragraph discusses budget. What is likely wrong with this structure?

   1. The budget information should be in a separate document
   2. The communities and their funding should be in the same paragraph, not separated
   3. The AI should not mention budget at all
   4. The narrative is properly structured

9. What type of concerns can AI NOT appropriately address when drafting recruitment narratives, even if they appear in the input notes?

   1. Statistical analysis of underrepresentation
   2. Budget calculations for recruitment materials
   3. Technical barriers to online survey completion
   4. Political, ethical, or relational concerns that require reading the room

10. A student uses AI to draft a recruitment equity narrative for their science fair project. The AI produces a summary that omits any questions for the reviewer to decide. What is the main issue with this output?

    1. The AI should not be used for student projects
    2. The output is missing the explicit decisions or asks the reviewer must resolve, which is a required format element
    3. The AI should have written a complete final document
    4. The summary is too short

11. What distinguishes a useful AI-drafted equity narrative from one that merely sounds inclusive but fails practically?

    1. Length and detail of the document
    2. Inclusion of specific funding commitments for community partners
    3. Use of diverse and representative vocabulary
    4. Number of communities mentioned

12. When might AI be least effective at improving a draft recruitment equity narrative?

    1. When the notes include multiple spreadsheet attachments
    2. When the notes discuss which stakeholders should be consulted before finalizing the plan
    3. When the notes are already polished and complete
    4. When the notes contain disorganized data about past recruitment

13. What should happen after receiving an AI-drafted inclusion-plan summary before submitting it to a funder?

    1. Delete any sections the AI marked as uncertain
    2. Review and resolve the explicit decisions or asks the AI surfaces
    3. Have the AI revise it until all questions are resolved
    4. Submit it immediately as the AI produced it

14. Why is it important that an AI-drafted equity narrative includes caveats within its substantive points?

    1. To acknowledge uncertainty and limitations in the recruitment plan
    2. To make the document appear more honest
    3. To reduce the word count
    4. To meet formatting requirements

15. A researcher inputs their notes about recruitment barriers into an AI tool. The AI produces a summary that presents these barriers as solved problems. What went wrong?

    1. The AI should have deleted the barrier section
    2. The AI should not be used for barrier analysis
    3. The researcher should have provided more notes
    4. The AI likely glossed over unresolved questions rather than surfacing them