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Human-AI Task Allocation Simulator

Run the Human-AI Task Allocation Simulator Fullscreen

About This MicroSim

This interactive simulation enables students to experiment with different human-AI task allocation strategies and observe their impact on productivity, quality, and cost metrics. The simulator demonstrates key concepts from Chapter 9: Future of Work and Workforce Transformation.

Iframe Embedding

You can include this MicroSim on your website using the following iframe:

<iframe src="https://dmccreary.github.io/Digital-Transformation-with-AI-Spring-2026/sims/human-ai-task-allocation/main.html"
        height="602px"
        width="100%"
        scrolling="no">
</iframe>

How to Use

  1. Select a Scenario: Choose from Customer Service, Financial Reports, Code Review, or Content Creation scenarios
  2. Adjust Task Rate: Control how quickly new tasks arrive (1-10 tasks per minute)
  3. Set Complexity: Adjust the average complexity of incoming tasks
  4. Observe Allocation: Watch how tasks are automatically allocated to Human, AI, or Collaborative processing
  5. Monitor Metrics: Track quality scores, costs, and throughput in real-time

Controls

Control Description
Scenario Select the work context (affects AI/human strengths)
Task Rate Number of new tasks generated per minute
Complexity Base complexity level for generated tasks
Start/Pause Toggle simulation running state
Reset Clear all tasks and metrics

Key Concepts Demonstrated

  • Task Complexity Analysis: Simple tasks route to AI, complex tasks to humans
  • Collaborative Allocation: Medium-complexity tasks benefit from human-AI partnership
  • Quality vs. Cost Tradeoffs: Observe how allocation decisions affect both metrics
  • Scenario-Specific Optimization: Different work contexts favor different allocation strategies

Allocation Logic

The simulator uses automatic allocation based on task complexity:

Complexity Allocation Rationale
1-3 (Low) AI Only High AI efficiency, low cost
4-7 (Medium) Collaborative Benefits from combined strengths
8-10 (High) Human Only Requires judgment and expertise

Learning Objectives

After using this simulator, students should be able to:

  • Apply (Bloom's L3): Apply collaboration principles to task allocation decisions
  • Analyze (Bloom's L4): Analyze the tradeoffs between quality, cost, and throughput
  • Evaluate (Bloom's L5): Evaluate which tasks benefit from human vs. AI processing

Lesson Plan

Activity 1: Baseline Observation (5 minutes)

Run the simulation with default settings and record the metrics after 2 minutes.

Activity 2: Scenario Comparison (10 minutes)

Switch between all four scenarios while keeping other settings constant. Compare: - Which scenario has highest quality? - Which scenario has lowest cost? - How does AI strength vary by domain?

Activity 3: Complexity Impact (10 minutes)

Keep the scenario fixed but vary the complexity slider from 1 to 10. Observe: - How does allocation distribution change? - What happens to quality at extreme complexity settings? - How does cost scale with complexity?

Discussion Questions

  1. Why might simple tasks be better handled by AI?
  2. What risks exist when AI handles high-complexity tasks?
  3. How might you design hybrid workflows for your organization?

References

  1. Brynjolfsson, E., & McAfee, A. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future. W.W. Norton.
  2. Daugherty, P. R., & Wilson, H. J. (2018). Human + Machine: Reimagining Work in the Age of AI. Harvard Business Review Press.
  3. Raisch, S., & Krakowski, S. (2021). Artificial Intelligence and Management: The Automation-Augmentation Paradox. Academy of Management Review, 46(1).

Self-Assessment Quiz

Test your understanding of human-AI task allocation principles.

Question 1: According to the simulation's allocation logic, which complexity level tasks are best suited for AI-only processing?

  1. High complexity (8-10)
  2. Medium complexity (4-7)
  3. Low complexity (1-3)
  4. All tasks regardless of complexity
Answer

C) Low complexity (1-3) - Simple, routine tasks with clear rules and low ambiguity are efficiently handled by AI alone, with high accuracy and low cost.

Question 2: What type of tasks benefit most from collaborative human-AI processing?

  1. Very simple routine tasks
  2. Medium complexity tasks that benefit from both human judgment and AI efficiency
  3. Tasks that require no thinking
  4. Tasks that cannot be defined
Answer

B) Medium complexity tasks that benefit from both human judgment and AI efficiency - Collaborative allocation leverages AI strengths (speed, consistency) combined with human strengths (judgment, context awareness) for optimal results.

Question 3: Why are high-complexity tasks typically allocated to humans in this model?

  1. AI cannot process any complex information
  2. These tasks require judgment, expertise, and handling of ambiguity that humans excel at
  3. High-complexity tasks are always faster for humans
  4. There is no reason; it is random allocation
Answer

B) These tasks require judgment, expertise, and handling of ambiguity that humans excel at - Complex tasks often involve nuanced decision-making, ethical considerations, and handling exceptions that require human cognitive capabilities.

Question 4: What is the main trade-off organizations face when allocating tasks between humans and AI?

  1. Speed vs. color preferences
  2. Quality vs. cost vs. throughput
  3. Building size vs. employee count
  4. Marketing vs. sales
Answer

B) Quality vs. cost vs. throughput - Organizations must balance achieving high quality outcomes, controlling costs, and processing tasks efficiently when designing human-AI workflows.

Question 5: How does the simulation help organizations design better workflows?

  1. By automatically firing employees
  2. By allowing experimentation with different allocation strategies to observe outcomes
  3. By eliminating all human work
  4. By making all decisions random
Answer

B) By allowing experimentation with different allocation strategies to observe outcomes - The simulator enables safe experimentation to understand how different task rates, complexity levels, and allocation rules affect key metrics before implementing changes in real workflows.