Denver Science and Evidence
Denver Schools: AI Literacy Through Science and Evidence
Teach Denver students to distinguish measured data from generated guesses, verify science claims, and plan an evidence-focused AI school assembly.
By Jim Jordan · · 4 min read
Easy-read summary
The big idea for Denver, Colorado schools
Denver offers familiar contexts for discussing weather, water, energy, and outdoor decisions. Those subjects make AI literacy tangible: a smooth explanation about a natural process can still include an unsupported assumption. Middle and high school learners need to distinguish what they observed, what a source established, and what a tool merely suggested. An AI presentation can introduce that vocabulary, while science classes provide repeated practice. The lessons below are examples to adapt, not Denver Public Schools policy or safety instructions. Follow approved-tool guidance and use instructor-reviewed references whenever a task involves real equipment, health, travel, or outdoor conditions.
Teach students to disclose AI help when a teacher or assignment requires it.
Use AI as a coach for questions, practice, feedback, and revision—not as a ghostwriter.
Verify important AI claims with reliable sources before sharing or submitting them.
Protect student privacy by avoiding personal, family, medical, or school-identifying details in prompts.
Section 2
Keep observations separate from generated suggestions
Give students a table with three columns: observed, sourced, and proposed. A measurement they actually recorded belongs in observed, along with its units and method. Information read in a reliable reference belongs in sourced. A hypothesis or generated explanation belongs in proposed until it is tested or verified. Students should never fill a gap in a lab notebook with invented readings, interviews, or citations. Missing data is a limitation to explain, not an invitation to manufacture a tidy result. A tool may help a learner formulate the next question if the assignment allows it, but it cannot retroactively create an experiment the class did not perform.
Section 3
Audit a weather explanation
Use a fictional AI response about a weather pattern and ask the class to identify its claims. Provide teacher-selected reference material, then compare the explanation with the evidence. Students note which claims are supported, which are oversimplified, and which cannot be checked from the supplied material. Ask what location and date matter to the answer; a general explanation may not describe conditions in a specific place. The exercise should use historical or teaching examples rather than live advice for an outing. Younger students can work with a shorter passage and a simple supported-or-unverified label. Older learners can examine assumptions and uncertainty in more detail.
Section 4
Treat outdoor safety as a human responsibility
AI output should not be the final authority for outdoor safety, emergency response, or a science demonstration. Teach students to use established school procedures, qualified adults, official alerts, and current approved references. A recommendation can sound reassuring while omitting the student's experience, equipment, access needs, or changing conditions. Practice recognizing when a question should be escalated rather than answered by a chatbot. Protect privacy too: do not upload precise student locations, itineraries, medical information, or identifiable group records to an unapproved system. A useful habit is to stop when uncertainty has consequences and ask the person responsible for the activity.
Section 5
Build an honest lab reflection
An honest lab reflection can answer four questions: What did I measure? What did I expect? What evidence explains the difference? What remains uncertain? If AI was allowed, add a focused note about its role. Students can explain why they rejected a generated explanation or corrected a unit conversion. Teachers should assess the reasoning alongside the final report and provide an equivalent path without AI. A quick oral check can confirm whether the learner understands the method. The goal is not a perfect-looking graph. It is evidence that the student can distinguish observation from explanation and take responsibility for a conclusion.
Student prompt examples
- “Coach me, but do not complete the assignment for me.”
- “Ask me three questions before giving suggestions.”
- “Show what might be wrong or missing in this answer.”
Section 6
Connect an assembly to the next science lesson
For a Denver AI school assembly, tell Jim Jordan's office which grade levels and science or research habits you want the keynote to reinforce. Include date options, audience size, location, and whether staff learning is part of the plan. A department could choose one observed-sourced-proposed activity to use afterward. Students then hear the assembly language again when they complete an actual investigation. The Colorado school-speaker page and follow-up library below can support planning. Email office@jimmyai.ca to discuss availability and a format that helps your learners become more careful investigators rather than more passive consumers of generated answers.
Action checklist
Use this after the assembly
- ●Create green, yellow, and red examples of acceptable AI help.
- ●Give students simple prompt language such as: coach me, do not complete it.
- ●Ask students to cite or explain how AI was used when appropriate.
- ●Revisit AI expectations during major writing, research, coding, and media projects.
Plan the next step for your school
Sources and citations