Students will enter the project by examining concrete AI writing ethics scenarios, comparing stakeholder perspectives, and using QFT to generate and narrow researchable questions that connect an urgent issue to a real audience for the white paper.

Students will narrow broad AI ethics interests into a clear, researchable problem statement for a white paper by comparing stakeholder needs, testing question quality, and defining an arguable issue worth investigating.

Students will generate possible white paper directions by comparing stakeholder needs, testing claim options against evidence, and selecting a focused, arguable issue for the next drafting phase.

Students will turn their refined research and stakeholder analysis into an initial white paper draft, using source-to-claim mapping, counterargument planning, and structured SWOC feedback to build a defensible argument for a real audience.

Students will test their white paper drafts, evidence maps, and stakeholder briefing ideas with peers and community-connected users, gather structured feedback on argument quality and audience fit, and make immediate revisions that strengthen claims, evidence, ethics, and clarity before the next critique phase.

Students will use structured critique routines to test the strength of their white paper arguments, source-to-claim evidence maps, and stakeholder recommendations, then make targeted revisions based on peer, teacher, and audience-fit feedback.

Students will synthesize their research and revision journey into public-facing final products, present their recommendations to authentic stakeholders, and document how their thinking about responsible AI use changed across the project.