Prototype Creation

Luke shared a live demo of the tool, which asks users a series of questions about their school context. It’s clean, intuitive, and user-friendly.

Claire shared how easy it was to use. Sean and Yash suggested features like post-generation editing and chatbot-assisted refinements. Dhruv emphasized intentionality in how we structure questions, breaking down broad categories like "bias" into meaningful, actionable components. 

We discussed not just technical improvements but deeper philosophical design choices. For example, should schools be asked to reveal their names? Would that make administrators uncomfortable? Would an anonymous, template-style output offer more privacy—and in turn, more adoption?

We discussed creating a tiered model.

• Tier 1: A basic draft generated from user inputs.
• Tier 2: Customization options like color-coded recommendations or editing tools.
• Tier 3: A premium, concierge-style experience with a chatbot, school branding, and tailored support.

What Comes Next
The students have now divided into three key task forces:

1. Refine the Policy Template – Reviewing and expanding categories to include AI Literacy, Accessibility, and Transparency.
2. Build the Ideal Output – Creating a “perfect” policy outline to guide prompt engineering and tool design.
3. Research Legal and Ethical Contexts – Mapping state and federal laws, age-based guidelines, and best practices.

We’re also evaluating various large language models (LLMs), such as Claude, Gemini, and ChatGPT, to determine the best fit for our needs.