The AI Delegation Paradox: Why Passive Tools Cultivate Active Humans (Series 1/3)

• Target Audience: Software Engineers, Developers, IT Professionals interested in AI utilization
• Prerequisites: Basic experience with AI tools like GitHub Copilot, ChatGPT, Claude, etc.
• Reading Time: 15 minutes
• Series: Part 1 of 3

About This Series

“Delegating to AI will reduce your thinking ability”—Is this warning true?

This series (3 parts) demonstrates, based on the latest cognitive science research, that the premise of this warning is wrong. Depending on usage, AI can become either a passive answer-producing machine or a catalyst that cultivates active problem solvers.

Series Structure:

• Part 1 (This Article): Why Passive Tools Cultivate Active Humans
• Part 2: People Who Grow with AI vs. Those Who Don’t—The Science of Guided Use
• Part 3: AI Strengthens Knowledge, Humans Strengthen Intelligence—Future Engineering

Overview

“Delegating to AI will reduce your thinking ability”—This warning has been repeatedly voiced in education and industry. And indeed, multiple studies support it.

However, there’s a fundamental fact being overlooked here: AI doesn’t act on its own.

A calculator won’t calculate anything if you just input “3.” A search engine won’t return anything if you just say “search.” Similarly, ChatGPT and GitHub Copilot are useless without clear instructions.

“Delegating” actually requires sophisticated cognitive work.

• What do you want to achieve (goal)
• What is the problem (problem definition)
• How to break it down (task decomposition)
• How to explain it (verbalization)

These are what prompt engineering research calls “high metacognitive demands.” A 2024 CHI Conference paper notes that at the prompt creation stage, “self-awareness of the task goal—that is, knowing precisely what one wants to achieve, breaking it down into subgoals and subtasks, and explicitly verbalizing it for effective prompts—is necessary”¹.

In this article (Part 1), we examine the AI Delegation Paradox—how passive tools cultivate active problem solvers.

Part 1: To Delegate, You First Need to Think

When AI Won’t Answer, Your Understanding Is Tested

Example of a bad prompt (try it yourself):

Find problems from massive log files

ChatGPT/Claude’s response (probably):

What kind of problems are you looking for?
What is the log file format?
What is the file size?
What is the definition of "problem"?

AI assumes nothing. It demands you clarify everything.

This isn’t inconvenient. This is AI’s educational value.

Cognitive Demands of Prompt Engineering

Research by Tankelevitch et al. (2024) shows that generative AI use imposes metacognitive demands at three stages¹:

1. Prompt Formulation Stage

• Self-awareness of task goal
• Decomposition into subgoals and subtasks
• Explicit verbalization

2. Output Evaluation Stage

• Judging validity of AI output
• Detecting hallucinations (plausible falsehoods)
• Considering alternatives

3. Automation Strategy Stage

• Iterative improvement of prompt strategies
• Deciding what tasks to delegate to AI and what to do yourself
• Deciding AI integration into workflow

Important point: These are all skills necessary for “delegation.”

Writing Good Prompts Requires Understanding the Problem

Prompt engineering research (2023-2024) shows that the iterative prompt refinement process is important. According to Hwang et al. (2023), prompt literacy is “the ability to iteratively refine prompts to achieve desired results,” and Federiakin et al. (2024) note that this process is related to cultivating critical thinking and problem-solving skills². When expected responses aren’t obtained, learners must analyze why and refine prompts, and this process develops cognitive skills.

In other words:

• Vague understanding → Vague prompt → Useless output
• Clear understanding → Clear prompt → Useful output

AI users have their level of understanding made visible through prompt quality.

Real Example: GitHub Copilot Reveals “Your Design Skills”

Scenario: You want to implement an authentication system

Vague comment:

# User authentication

Copilot’s suggestion:

def authenticate_user(username, password):
    # Implementation omitted
    pass

→ Unusable. Security requirements and error handling unclear.

Clear context:

"""
JWT-based authentication service.

Requirements:
- Password hashing: bcrypt with cost factor 12
- Token expiration: 24h for access, 7 days for refresh
- Algorithm: RS256 (asymmetric)
- Error handling: 401 for invalid credentials, 403 for expired tokens
- Rate limiting: max 5 attempts per minute per IP

Security considerations:
- No password in logs
- Timing attack resistance for password comparison
- Token rotation on refresh
"""

class AuthService:
    # Copilot generates high-quality code from this context

What’s the difference? Whether the user has structured the problem.

Copilot isn’t magic. It’s a mirror reflecting your design.

Part 2: Explaining to AI = Accelerated Learning

What Is the Feynman Technique

A learning method proposed by physicist Richard Feynman:

1. Choose a concept
2. Write as if explaining to someone (a child)
3. Identify parts you can’t explain
4. Relearn those parts
5. Repeat

The core: “Can’t explain = Don’t understand”

Learning by Explaining: Remarkable Performance Improvement

In the classic study by Chi et al. (1994), among students prompted to self-explain during learning, those who generated many explanations (high explainers) generated approximately 3 times more inferences (87 vs 29) and showed deeper understanding and problem-solving ability compared to those who generated few explanations (low explainers)³.

However, there’s an important condition: Only effective if you identify and address knowledge gaps.

Problem: Traditionally, this method had limitations:

• Needed someone to listen to explanations (not always available)
• Feedback quality depended on the other person’s knowledge
• Depended on the other person’s patience (can’t repeat many times)

Why AI Is a Game Changer

AI (ChatGPT, Claude, etc.) is the perfect partner for the Feynman Technique:

✅ Available 24/7

• Will listen to explanations even at 3 AM

✅ Infinite patience

• Won’t complain even after 100 explanations

✅ Consistent feedback

• Points out “XYZ is unclear in that explanation”

✅ Exposes knowledge gaps

• Vague explanation → “What specifically does ○○ refer to?”
• You can’t answer → Recognize gap → Relearn

Practical Example: Testing Concept Understanding with AI

Task: Learn Kubernetes deployment strategies (RollingUpdate vs Recreate)

Traditional learning:

1. Read documentation
2. “I understand (I think)”
3. Confused when actually using it

Feynman Technique with AI:

[You → ChatGPT]
Please teach me the difference between RollingUpdate and Recreate in Kubernetes,
explaining the Pod update process step by step.
Also, listen to my explanation and point out any inaccuracies.

My understanding:
RollingUpdate deletes old Pods one by one while starting new Pods.
Recreate deletes all old Pods first then starts new Pods.

[ChatGPT → You]
Mostly correct, but there are some inaccuracies:

1. RollingUpdate isn't "delete then start" but
   "start new Pod, then delete old Pod after health check success."
   Controlled by maxSurge and maxUnavailable parameters.

2. Regarding downtime:
   - RollingUpdate: No downtime (some Pods always running)
   - Recreate: Has downtime (between full deletion and full startup)

3. Different use cases?
   - RollingUpdate: Stateless apps, availability priority
   - Recreate: Database migrations, etc., where version mixing is problematic

Try explaining again including these details.

[You → ChatGPT]
(Attempt explanation again → Understanding deepens further)

Result:

• Recognized details missing from initial explanation (maxSurge, maxUnavailable, downtime)
• Feedback from AI clarifies vague understanding
• Re-explaining reinforces memory

Important: This is an “explain to AI” approach, not “ask AI.” It’s active.

Part 3: Passive Tools Cultivate Active Humans

AI’s Essential Constraint Trains Users

Calculators don’t calculate on their own. Search engines don’t search on their own. AI doesn’t think on its own.

All tools are passive.

However, this passivity demands user activity.

AI Use Improves User Engagement

A 2025 study (452 healthcare workers) showed that AI-employee collaboration promotes the following⁴:

• AI mastery goal: Motivation to master AI
• Paradox mindset: Thinking that integrates contradictory elements (human judgment vs AI proposals)

These elements increase active engagement in AI collaboration.

Why?

To master AI:

1. Clearly define the problem
2. Understand AI’s capabilities and limitations
3. Critically evaluate outputs
4. Run feedback loops

All of these are active problem-solving skills.

Importance of Understanding AI’s Limitations

AI can answer individual questions accurately, but comprehensive judgment is difficult:

• ✅ “What does this syntax mean?” → Can answer accurately
• ✅ “What are the merits of this technology?” → Can answer accurately
• ⚠️ “In this situation, which technology should I choose?” → Can’t fully understand your organization’s constraints, team skills, or long-term goals

By understanding this limitation, an appropriate collaborative relationship emerges where AI is used as an “advisor” and humans make final judgments.

Students Can Recognize Their Own Over-Reliance

The assumption that “people who delegate to AI don’t realize their dependency” is wrong.

A 2025 study showed that students can metacognitively recognize their AI over-reliance to some extent (r = 0.355, p = .011)⁵.

What this means:

• AI users are partially aware they’re “relying too much on AI”
• This self-awareness becomes a foundation for educational intervention
• With metacognitive awareness, usage can be improved

Prompt Engineering Definition Shows “Activity”

Prompt engineering is defined in research as²:

“The ability to verbalize the problem, make its context and constraints explicit, and enable AI to respond quickly and accurately”

“Verbalize the problem”—This is not a passive act.

• What is the problem? (Problem definition)
• What do you want to achieve? (Goal setting)
• What constraints exist? (Constraint clarification)
• How will you evaluate? (Success criteria)

These are all core skills of project management, software design, and problem-solving.

Forcing Activity: AI Does Nothing Without Instructions

Compare with traditional tools (IDE, debugger, profiler):

IDE (VSCode, etc.):

• Once started, syntax highlighting is automatic
• Once saved, auto-formatting runs
• If there are errors, red lines appear

AI (GitHub Copilot, ChatGPT):

• Nothing happens just by starting
• No suggestions without writing comments
• No response without prompts

The difference is clear: AI demands explicit intent expression from users.

This “forced activity” trains AI users.

Bootstrapping Activity: Starting from Zero

However, an important question arises here: If you’re not active to begin with, can you not use AI?

The answer is “No.” In fact, AI can also function as a catalyst that generates activity from a state of no activity.

Scenario: Completely stuck engineer

[Situation]
Working on implementing a new feature but don't know where to start.
Can't think of anything. Completely stuck.

[Traditional approach (without AI)]
- Read documentation → Too vast, don't know where to start reading
- Ask colleague → Not even clear what to ask
- Try writing code → No direction, wasted trial and error

→ Activity remains zero, frustration accumulates

“Bootstrapping activity” with AI:

[Step 1: Try explaining the situation]
Type into ChatGPT (can do this even in completely passive state):

"I want to implement authentication but don't know where to start.
Requirements are ○○, tech stack is △△.
Was told security is important but don't know what to do..."

[Step 2: Thought gets organized in the process of explaining]
While typing, you notice:

"Oh wait. What exactly are the requirements?
- Password auth? Or OAuth?
- Session management after login?
- Need password reset feature?"

→ **What's unclear** becomes visible (budding of activity)

[Step 3: AI's response becomes a spark for thinking]
ChatGPT's response:

"Authentication method selection is the first step.
- JWT (JSON Web Token) based: Stateless, scalable
- Session based: Simple, server-side management

Considering tech stack (△△), JWT is suitable.
Security requirements:
1. Password hashing (bcrypt recommended)
2. HTTPS required
3. CSRF countermeasures
..."

[Step 4: Activity expands in chain reaction]
Engineer's thinking:

"I see, JWT. Then:
- What about token expiration?
- Need refresh tokens?
- What libraries exist?
- How to do security audit..."

→ Questions keep arising (completely active state)

[Step 5: To structured problem-solving]
Ask AI again, this time with structured question:

"Want to plan JWT authentication implementation. Considering:
1. Access token expiration: 15 minutes
2. Refresh token: 7 days
3. Tech stack: Node.js + Express + PostgreSQL
4. Integration with existing system: REST API

Tell me implementation steps and points of caution"

→ Completely in active problem-solving mode

Key mechanisms:

1. Externalization of thinking
   • The act of “explaining to AI” forces verbalization of vague mental state
   • In the process of verbalization, what the problem is becomes clear
2. AI as Scaffolding
   • AI’s response provides “scaffolding” for thinking
   • Not starting completely from zero, transformed into task of “evaluating AI’s proposal”
   • Evaluation is easier than thinking from scratch (lowers activity barrier)
3. Development of dialogical thinking
   • Thinking deepens through AI → you → AI → you… loop
   • Variation of Feynman Technique³: Understanding deepens through explanation
   • However, initially explaining “what you don’t know” rather than “what you understand”
4. AI version of rubber duck effect
   • Programmer’s “explain to rubber duck and find solution” effect
   • AI is better than rubber duck: It responds
   • Response becomes a spark for thinking

Important distinction:

❌ Passive use (activity doesn’t emerge):

"Implement authentication feature"
→ AI returns entire code
→ Copy-paste and done
→ Activity remains zero

✅ Bootstrapping activity (activity emerges):

"I want to implement authentication but ○○ is unclear..."
→ Thinking gets organized in process of explaining
→ AI's response generates new questions
→ Activity expands in chain reaction

This “bootstrapping of activity” may be AI’s most revolutionary aspect.

Traditionally, it was thought “you can’t learn unless you’re active.” However, AI can cultivate activity from a state of no activity.

Practical Guide: AI Utilization That Generates Activity

How to apply the theory we’ve seen into practice. Here’s a concrete guide.

1. AI Utilization Template When Stuck

When “you don’t know what to do,” that’s exactly when to use AI. Use this template:

Template: Situation Organization Prompt

[Current State]
- What you want to do: [specific goal]
- Tech stack: [technologies being used]
- Progress: [how far you've gotten]
- Where stuck: [what's unclear]

[What you've tried]
- [Attempt 1]: Result → [why it didn't work]
- [Attempt 2]: Result → [why it didn't work]

[What you don't know]
1. [Question 1]
2. [Question 2]
3. [Question 3]

First, tell me where to start, prioritized.

Effect:

• Just writing along the template organizes thinking
• In the process of verbalizing “what you don’t know,” some parts may self-resolve
• AI’s response becomes concrete guidance for next steps

2. Feynman Technique Practice Prompt Collection

Pattern A: Understanding Check

I'll explain my understanding of [concept name].
Please specifically point out inaccuracies and missing points.

[My understanding]
[explain in your own words]

[Points I especially want to confirm]
1. [uncertain point 1]
2. [uncertain point 2]

Pattern B: Step-by-step Deep Dive

I want to understand [concept name] step by step.

Step 1: First teach me the basic definition
→ [read and understand AI's response]

Step 2: So why is [concept name] necessary? What problems does it solve?
→ [read and understand AI's response]

Step 3: Give me 3 real use cases
→ [read and understand AI's response]

Step 4: Let me confirm my understanding. [concept name] is... (explain in own words)
→ Is this accurate?

Pattern C: Teaching Others Simulation

I need to teach [concept name] to a junior engineer.
Please evaluate the following explanation draft.

[My explanation draft]
[your planned explanation]

[Points for evaluation]
1. Is it understandable to a beginner?
2. Are there any important points missing?
3. Are there expressions that could cause misunderstanding?

3. Prompt Patterns That Generate Activity

❌ Pattern to avoid (passive):

Do [task]

→ AI does everything → No learning

✅ Recommended Pattern 1 (Problem Structuring Type):

I'm working on [task].

[Current Analysis]
- [current situation]
- [constraints]
- [goal]

[My Hypothesis]
[reason why I think this approach is appropriate]

Is this hypothesis valid? Are there alternatives?

✅ Recommended Pattern 2 (Step-by-step Support Type):

I want to implement [task] myself.
Don't write the code, just teach me:

1. Rough implementation steps (about 5 steps)
2. Points to watch for in each step
3. Common pitfalls

Then I'll implement it myself.

✅ Recommended Pattern 3 (Review Request Type):

I've implemented [task].

[Implementation Code]
[code]

Please review from these perspectives:
1. Any security issues?
2. Any performance improvements?
3. Any missed edge cases?
4. Any better design alternatives?

4. Self-Assessment Checklist: Activity Score

Self-check after AI use:

Thought Organization (3 points max)

• Noticed what was unclear in the process of explaining to AI (1 point)
• Understanding deepened by improving prompt (1 point)
• New questions arose (1 point)

Activity Expression (3 points max)

• Critically evaluated AI’s response (1 point)
• Thought of alternatives yourself (1 point)
• Judged what to do next yourself (1 point)

Learning Depth (4 points max)

• Can explain AI’s response in your own words (1 point)
• Understood why this approach (1 point)
• Seems applicable to other cases (1 point)
• Seems like you could implement yourself next time (1 point)

Score Assessment:

• 8-10 points: Excellent! Using actively
• 5-7 points: Good direction. Pursue “why?” more
• 0-4 points: Becoming passive. Try Pattern 2 (Step-by-step Support Type)

5. Weekly Reflection: Tracking Activity Growth

Every Friday, do this in 5 minutes:

Reflect on this week’s AI use:

1. Most helpful AI use?
   • In what situation?
   • What prompt did you use?
   • What did you learn?
2. Failed AI use?
   • What didn’t work?
   • Were you being passive?
   • How to improve next time?
3. Feeling of growth
   • What can you solve yourself this week?
   • Things you asked AI last week—could you do yourself this week?
   • Did prompt quality improve?

Recording this allows you to feel significant growth after a few months.

Summary: Passive Tools Cultivate Active Humans

What we’ve seen in this article (Part 1):

1. To Delegate, You First Need to Think

Prompt engineering imposes “high metacognitive demands”¹:

• Problem clarification
• Goal setting
• Task decomposition
• Context verbalization

AI is a mirror that makes your level of understanding visible.

2. Prompt Quality = Visualization of Understanding

Vague understanding → Vague prompt → Useless output → Feedback → Improvement

This loop cultivates active problem solvers.

3. Explaining to AI = Practicing the Feynman Technique

Learning through explanation → About 3x more inference generation, deeper understanding³ AI is a perfect explanation partner available 24/7.

4. Bootstrapping Activity

The most revolutionary discovery: AI can be a catalyst that generates activity from a state of no activity.

• Thinking gets organized in the process of explaining the situation
• AI’s response becomes a spark for thinking
• Activity expands in chain reaction

Next Step

However, an important question remains:

“But aren’t there studies showing AI reduces thinking ability?”

That’s right. Even with the same tool, opposite results depending on usage.

Series Part 2 “People Who Grow with AI vs. Those Who Don’t—The Science of Guided Use” covers the decisive difference between guided and unguided use, the three paths after receiving a response, and factors that separate those who grow from those who don’t, along with the latest research.

References

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Series Navigation

• Part 1: Delegating to AI Requires Thinking First (This Article)
• Part 2: People Who Grow with AI vs. Those Who Don’t—The Science of Guided Use
• Part 3: AI Strengthens Knowledge, Humans Strengthen Intelligence

1. The Metacognitive Demands and Opportunities of Generative AI - Tankelevitch, L. et al. (2024). CHI Conference on Human Factors in Computing Systems. [Reliability: High] ↩︎ ↩︎² ↩︎³

2. Prompt engineering as a new 21st century skill - Federiakin, D., Molerov, D., Zlatkin-Troitschanskaia, O., & Maur, A. (2024). Frontiers in Education. [Reliability: High] ↩︎ ↩︎²

3. Eliciting self-explanations improves understanding - Chi, M. T. H., de Leeuw, N., Chiu, M.-H., & Lavancher, C. (1994). Cognitive Science, 18(3), 439-477. DOI: 10.1016/0364-0213(94)90016-7. [Reliability: High] (Classic study in cognitive science on the self-explanation effect) ↩︎ ↩︎² ↩︎³

4. Dynamics of user engagement: AI mastery goal and the paradox mindset in AI–employee collaboration - International Journal of Information Management (2025). [Reliability: High] ↩︎

5. Students’ Reliance on AI in Higher Education: Identifying Contributing Factors - Pitts, G., Rani, N., Mildort, W., & Cook, E.-M. (2025). arXiv preprint. [Reliability: Medium-High] ↩︎