This presentation explores how AI can support student sensemaking by guiding learners through the process of building models from regional, relevant datasets. Participants will see how interdisciplinary connections between science and statistics can be strengthened when students use AI as a scaffold to develop spreadsheet formulas, test predictions, and refine hypotheses. Rather than conducting analysis for them, AI prompts students with guiding questions and formula structures that empower them to explore correlations and relationships independently. This approach emphasizes student agency, encouraging learners to make predictions, brainstorm modeling strategies, and iteratively improve their work. By situating the activity in regional contexts, the project ensures relevance and authenticity, helping students connect data patterns to real-world phenomena. Educators will leave with strategies for integrating AI into classroom projects that deepen inquiry, foster statistical reasoning,
TAKEAWAYS:
Participants will go on a data adventure exploring real datasets, uncovering variable relationships, and using AI as a supportive tool. This journey, appropriate for grades 6-12, deepens inquiry, strengthens modeling skills, and inspires more meaningful, data-driven learning.
SPEAKERS:
Melissa Stirling, Lora Gibbons, Theresa Goltermann