Smart Data, Smarter Analysis

Presented by James CantonwineConference SessionFor AI Innovation Summit

Smart Data, Smarter Analysis: Leveraging AI for Educational Insights

Scheduled for February 4 & 6, 2025, this presentation explores how Artificial Intelligence (AI), specifically Large Language Models (LLMs), can be used to enhance data analysis in educational settings. It focuses on practical applications, data privacy, and the importance of explicit instruction when interacting with AI.

Key Takeaways:

  • LLMs as Code-Writing Calculators: LLMs are not calculators in the traditional sense, but they can write code (Python, Java) to perform calculations and analyze data.
  • Data Privacy is Paramount: The presentation stresses the importance of following district guidelines and considering data identifiability when using AI tools.
  • Tiered Data Security Model: A four-tiered model for AI tool usage is presented:
    • Sandbox AI: Publicly available platforms (e.g., free versions of ChatGPT, Claude) for general use with non-sensitive data. Extreme caution is advised due to data exposure risks.
    • Lifeguard AI: Vetted education-specific tools (e.g., paid versions of ChatGPT, Claude, MagicSchool.ai, ClassCompanion, NotebookLM, Khanmigo) with data protections, suitable for de-identified, aggregated data.
    • Vault AI: Private AI environments (e.g., Amplify GenAI, custom-built solutions) within a district's secure infrastructure, allowing for the use of more detailed student data.
    • Pocket AI: Device-based AI tools that operate without network connectivity, offering the highest level of data privacy.
  • Amplify GenAI as a Vault AI Solution: The presentation highlights Amplify GenAI, an open-source project with instances of major LLMs hosted on district servers, ensuring no data is sent to AI developers.
  • AI as a Data Analysis Intern: The presentation positions AI as a capable but fallible intern, requiring clear instructions, review, and validation of outputs.
  • Explicit Instruction Matters: Following Rosenshine’s principles for direct instruction when interacting with the LLM.
  • The "Jagged Frontier": AI may excel at seemingly complex tasks while struggling with simpler ones, highlighting the need for human oversight.
  • Practical Use Cases: The presentation explores several practical applications of AI in data analysis:
    • Data Cleaning: Identifying inconsistencies, handling missing values, and standardizing data formats.
    • Open-Ended Survey Data Analysis: Summarizing themes, analyzing sentiment, and grouping responses.
    • Student Assessment Data Analysis: Calculating medians, identifying trends, and analyzing correlations.
    • School and District Trend Analysis: Analyzing enrollment trends, identifying growth or decline, and exploring demographic shifts.

Actionable Insights:

  • Understand LLM Capabilities: Recognize that LLMs excel at writing code for data analysis but require human validation.
  • Prioritize Data Privacy: Follow district guidelines and utilize the appropriate AI tool tier (Sandbox, Lifeguard, Vault, Pocket) based on data sensitivity.
  • Provide Explicit Instructions: Give clear, structured prompts to AI, similar to how you would instruct a human intern.
  • Review and Validate Outputs: Always critically evaluate AI-generated results, especially quantitative data, and look for code generation as a sign of more reliable analysis.
  • Explore Practical Applications: Experiment with using AI for data cleaning, survey analysis, assessment data analysis, and trend identification.
  • Leverage Amplify GenAI: Consider using Amplify GenAI as a secure platform for data analysis tasks involving more sensitive information.
  • Follow Rosenshine’s Principles of Instruction Use these techniques to give the AI explicit instructions for data anaylsis.

Looking Ahead:

This presentation empowers educators and administrators to leverage AI for more effective data analysis. By understanding the capabilities and limitations of LLMs, prioritizing data privacy, and providing explicit instructions, users can harness AI's power to gain valuable insights from educational data. The presentation emphasizes the importance of a thoughtful and informed approach to AI implementation, recognizing both its potential to enhance analysis and the ongoing need for human oversight and critical thinking.