Analyzing Soil Properties Using AI Tools

Example developed based on CLOs from: CEM 372 – Soil Mechanics.

Categories

• AI Integration Level: AI-Assisted
• Assessment Type: Formative
• Bloom’s Taxonomy: Analyze
• Skills Focus: Critical Thinking, Problem-Solving
• Domain: STEM

Purpose

This assignment helps students analyze soil properties by using AI tools to interpret sample data and identify patterns. It emphasizes critical thinking and problem-solving through real-world applications in soil mechanics.

Learning Outcomes

• Analyze soil properties based on provided sample data.
• Use AI tools to identify patterns and correlations in soil behavior.
• Reflect on the role of data analysis in soil mechanics and its real-world implications.

Instructions

Steps for Using Microsoft Copilot to Analyze Soil Sample Data

1. Prepare Your Data
   • Ensure the soil sample data is organized in a structured format (e.g., CSV,).
   • Include parameters such as grain size, moisture content, compaction level, and any other relevant properties.
2. Use Prompts to Analyze Data
   • Use Copilot to generate analysis code by typing natural language prompts or partial code snippets. Example prompts:
     • “Summarize grain size distribution for the soil samples.”
     • “Generate a graph to compare moisture content across different sample sites.”
     • “Identify trends in compaction level based on soil type.”
3. Refine Generated Code
   • Review the information generated by Copilot to ensure it aligns with your analysis goals.
   • Customize to include additional calculations or visualizations, such as histograms, scatter plots, or regression analysis.
4. Generate Insights and Visualizations
   • Use Copilot-generated content to produce descriptive statistics, correlations, and visualizations. Example outputs:
     • Grain size distribution histograms.
     • Scatter plots of moisture content versus compaction.
     • Correlation matrices to identify relationships between variables.
5. Document the Process
   • Include the code and outputs in your analysis report.
   • Use Copilot to help write documentation for the code by prompting:
     • “Add comments explaining each step in this data analysis script.”
6. Verify and Validate Results
   • Cross-check AI-generated insights with reference materials or manual calculations to ensure accuracy.
   • Reflect on the limitations of using AI for this analysis and document any adjustments made.

Example Prompts for Soil Analysis

1. “Write Python code to calculate the average moisture content for each soil type.”
2. “Visualize the relationship between grain size and compaction in the dataset.”
3. “Generate a report summarizing the key statistics for soil sample properties.”

Format (Final Deliverable)

1. Data Analysis
   • Use AI tools such as Microsoft Copilot to analyze soil sample data provided by the instructor.
   • Identify patterns, such as relationships between moisture content, grain size, and compaction.
2. Reporting
   • Write a 500-word report summarizing your findings, including graphs or visualizations generated from the analysis.
   • Include interpretations of the patterns and their implications for soil behavior in construction contexts.
3. Reflection
   • Write a 200-word reflection addressing:
     • How AI tools supported your data analysis process.
     • Insights gained about the importance of soil analysis in engineering.
     • Challenges faced and how you addressed them.

Grading Criteria

• Accuracy and depth of data analysis (40%)
• Quality and clarity of the report (30%)
• Effective use of AI tools with proper documentation (20%)
• Insightfulness of reflection (10%)

Resources

• Microsoft Copilot for analyzing sample data.
• Instructor-provided soil sample data for analysis.