Skip to content

Generating a CLD for Cutting AI Training

Prompt

I have placed the cld-schema.json file in the Files area. Now please generate a new CLD JSON file that conforms to this schema for the file edu-ai-training.md that is also in the Files area.

Note that within the edu-ai-training.md file we have the following:

The System Structure

**Budget Constraints → Cut AI Training Funding → Maintained Traditional Programs → Teacher Obsolescence & Exodus → Higher Costs & Worse Outcomes → More Training Budget Cuts **

Think carefully about this structure. It may not be correct. It might be too linear and not have the correct feedback loops in it. Revise it as necessary to reflect a high quality causal loop diagram with feedback loops.

Response

I'll analyze the system structure in the edu-ai-training.md file and create a comprehensive CLD JSON that properly captures the feedback loops. You're right that the current structure is too linear - let me identify the key feedback loops and create a proper causal loop diagram.I've created a comprehensive Causal Loop Diagram JSON file that properly captures the feedback dynamics of the AI training budget cuts scenario. Here are the key improvements I made to the original linear structure:

Key Feedback Loops Identified:

  1. Primary "Fixes That Fail" Loop (Reinforcing): Budget Pressure → AI Training Cuts → Teacher AI Competency ↓ → Teacher Confidence ↓ → Teacher Retention ↓ → Staffing Costs ↑ → Budget Pressure ↑

  2. Quality-Reputation Loop (Reinforcing): AI Competency → Educational Quality → District Reputation → Budget Available → Budget Pressure ↓ → AI Training Cuts ↓

  3. Retention-Cost Balancing Loop: Higher retention reduces costs, which could enable better training investment

Major Structural Improvements:

  • Added feedback mechanisms that were missing from the linear structure
  • Identified delay effects in competency building, quality improvement, and reputation changes
  • Created proper causal relationships between variables like teacher confidence, retention, and recruitment costs
  • Added leverage points at different levels (parameters, paradigms, delays)
  • Included educational scenarios for exploring different intervention strategies

The original structure was indeed too linear and missed the critical feedback loops that make this a true "Fixes That Fail" archetype. The new structure shows how the "quick fix" of cutting AI training creates a reinforcing loop that makes the original budget problem worse over time through increased staffing costs and reduced funding support.

This CLD now properly demonstrates the systems principle that quick fixes often fail because they don't address root causes and can create unintended consequences that make the original problem worse.