Build a Plan You Can Defend.
A civics decision game mapping stakeholders, evidence, and trade-offs
An issue in your community requires action. Generative AI can draft a polished, professional-sounding civic action plan in seconds — but this is a bypass trap. The plan arrives without the cognitive labor and research that makes a citizen truly informed.
In this game, you will navigate the C3 Framework Inquiry Arc. You must frame the problem, select credible evidence, map stakeholders, and weigh actions.
The temptation: Throughout the game, AI will offer you frictionless summaries and shortcuts. Accepting them will save time, but will dilute your Evidence, ignore critical Stakeholders, and outsource your Ownership. Interrogate the AI to earn a plan you can stand behind.
C3 Inquiry Arc
- 1. Frame the Question
- 2. Gather Evidence
- 3. Map Stakeholders
- 4. Choose Action
- 5. Review Plan
💡 Mentor Hint
▼Active Phase
The Dangerous Crossing
Students report near-misses at the main intersection near the school. You want to secure a safer crossing signal from the city.
How do you frame your civic investigation?
AI has drafted an instant summary of the traffic data and academic studies on phone distractions. Save time by using this ready-made template.
Audit the AI proposal text:
Friction Audit: Select the major design flaw in this draft plan before submitting it.
Friction Log & Analysis
Choose an option or evaluate the AI shortcut to see the reasoning analysis and impact on your plan's meters.
Civic Plan Audit
Your civic action plan has been finalized. Let's inspect your ownership and planning rigor.
Evidence Base
Measures if your plan relies on credible, triangulated, verified statistics and local surveys rather than national vibes or unverified summaries.
Stakeholder Breadth
Measures whose voices and interests are incorporated in the plan. High scores require consulting those most affected and marginalized.
Feasibility
Measures if your proposed action is realistic, targets the correct decision-making body, and operates within real-world scopes.
Plan Ownership
OwnedIndicates whether this plan is truly yours. Accepting pre-packaged AI plans outsources your agency, while interrogating details secures it.
Defensible Action Plan
Content...
The Trap of the Frictionless Plan
Generative AI is a powerful tool for drafting proposals, summarizing documents, and organizing tasks. However, in civics education, the process is the product.
When you let AI write the plan without interrogating its sources, mapping the local stakeholders yourself, or verifying the legal authorities, you ship a plan that is fluent but hollow. Real citizenship requires taking responsibility for the accuracy, equity, and viability of the claims you make.
Interrogating vs. Banning
This game models a balanced path. Banning AI hides its presence, but uncritical adoption surrenders cognitive labor. The ideal move is to use AI as a sparring partner — generating drafts and then subjecting them to intense human critique and local verification.
By forcing yourself to look for what the AI left out, you build the pedagogical friction necessary for real conceptual learning and ownership.
C.O.R.E. & H.E.A.R.T. Alignment
C.O.R.E. Critical Thinking & Engagement
Critical thinking is activated when you audit AI plans for omissions. Engagement is maintained by building the plan actively rather than passively receiving pre-packaged text.
H.E.A.R.T. Responsibility & Empathy
Responsibility means standing behind the viability of your plan. Empathy requires mapping stakeholders carefully, ensuring that those with the least institutional power are not omitted or tokenized.
Dissertation bridge
Public scholarship for Pedagogical Friction in the Age of Generative AI and Tertiary Algorithmicity — prompts about the proposed study, not findings from it.
- RQ1 — educator sensemaking: Where did the AI evidence-summary shortcut reduce friction, and what kept the inquiry intellectually owned by the learner?
- RQ2 — institutional conditions: Which institutional conditions made careful stakeholder mapping and feasible action planning possible, and which would constrain friction-preserving pedagogy?
- RQ3 — framework-aligned supports: What policy language, assessment expectations, and instructional-design supports would preserve authorship while reducing exclusionary friction?
Note: All scores, stats, and achievements are illustrative models of the pedagogical friction framework, not validated learning assessment instruments.