From Local Problems to Community Solutions: Join the Fall 2026 CIDDL AI Community Challenge
Authors: Matthew Marino; Matthew.Marino@ucf.edu
Secondary school teams will use evidence, human judgment, and artificial intelligence to design practical responses to authentic community needs.
What could students improve in their community if artificial intelligence became a thought partner rather than an answer machine?
The Center for Innovation, Design, and Digital Learning (CIDDL) invites students and teachers in Grades 7–12 to answer this question through the Fall 2026 AI Community Challenge. Teams nationwide will identify a consequential local issue, investigate its causes, and design an evidence-informed solution with support from artificial intelligence (AI). The challenge places human reasoning at the center of every submission.
Students will not compete on access to expensive computing systems or proprietary tools. They will compete through disciplined inquiry, critical AI literacy, feasible design, and authentic community impact. Strong entries will show how students tested AI-generated ideas, detected limitations or bias, verified important claims, and improved a proposed solution through human judgment.
A Challenge Grounded in Civic Agency
The challenge asks each team of three students and one teacher to examine a local problem through at least two distinct sources. Students might conduct interviews, complete observations, review public records, analyze community surveys, or consult local leaders. Teams will then use AI to extend their inquiry, compare possible approaches, and stress-test assumptions.
Each final proposal must include a practical action plan, an itemized estimate of initial costs, and a five-year community return-on-investment model. Teams should quantify direct benefits when credible data permit calculation. They should also explain important benefits without simple monetary values, including safety, accessibility, civic participation, environmental quality, or community trust.
Five Moves From Inquiry to Impact
- Discover: Identify a genuine community problem and verify its importance through two or more local sources.
- Investigate: Examine root causes, stakeholders, constraints, and existing responses. Narrow the problem until a team can address it through a realistic design.
- Partner with AI: Use AI to generate questions, compare approaches, test assumptions, and reveal overlooked considerations. Record pivotal prompts and moments when human reasoning redirected AI.
- Design: Develop a feasible solution, estimate implementation costs, calculate a five-year community return, and describe valuable outcomes without monetary equivalents.
- Tell the story: Create a compelling three-minute video featuring evidence, AI interactions, human decisions, the proposed model, and student reflection.
What the Process Could Look Like
Consider a ninth-grade science, technology, engineering, and mathematics class confronting severe traffic congestion near school arrival zones. Students first interview the school resource officer, consult neighborhood leaders, record one week of observations, and analyze municipal traffic data. Their evidence confirms recurring choke points, bus delays, unsafe crossings, and prolonged vehicle idling.
The students ask AI to compare school arrival models, generate overlooked questions, and locate strategies used in other communities. The team rejects impractical recommendations, screens outputs for bias, and verifies each consequential claim. Students then design a Smart Arrival Zone with revised traffic patterns, strategic signage, student-created quick-response code cards for verified walking and bicycling routes, and a digital channel for reporting recurring congestion.
The team estimates an initial cost of $4,800 and calculates direct returns through reduced staff traffic-duty hours and fewer transit delays. Students also document non-monetized value through safer crosswalk conditions and reduced emissions from idling vehicles. This model illustrates the challenge’s central premise: AI can expand inquiry, but students must supply local knowledge, ethical judgment, verification, and practical wisdom.
How Teachers Can Support Strong Student Work
- Help students transform broad frustrations into specific, solvable problems.
- Require local interviews, observations, or records before students begin working with generative AI.
- Ask teams to preserve pivotal prompts and explain how AI changed their questions or assumptions.
- Require verification for every consequential claim, estimate, and AI-suggested source.
- Press students to identify where their judgment made an AI-generated idea safer, fairer, more useful, or more feasible.
Recognition Will Emphasize Reasoning and Impact
CIDDL will recognize excellence across five categories: evidence-informed design, human-AI collaboration, innovation, community impact, and communication. Reviewers will examine each team’s local evidence, investigation methods, solution and budget, five-year value model, AI partnership, measurable outcomes, human oversight, and reflection.
Join the Fall 2026 Challenge
The final three-minute video gives students a chance to present a local problem, explain their investigation, show how AI informed their reasoning, and demonstrate how human judgment strengthened the final design. Prototype feedback or preliminary pilot results can further substantiate a proposal when feasible and appropriate. Interested? Reach out to Dr. Matthew Marino at Matthew.Marino@ucf.edu
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