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Changing How You Relate to AI: From Passive 'Using' to Active 'Nurturing'

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Changing How You Relate to AI: From Passive 'Using' to Active 'Nurturing'
  • Target Audience: Engineers who use AI tools (GitHub Copilot, ChatGPT, etc.) daily
  • Prerequisites: None (focuses on mindset rather than technical details)
  • Reading Time: 15 minutes

Overview

GitHub Copilot completes your code. ChatGPT solves your errors. It’s convenient.

But then you wonder: “Am I growing? Or am I just becoming dependent?”

AI is neither something to reject nor avoid. Like calculators and IDEs, it’s becoming ordinary infrastructure. What matters is “how you engage with AI.”

Research from 2024-2025 shows there are two paths in how we relate to AI. One leads to passive “dependency” (skills atrophy). The other leads to active “augmentation” (capabilities grow).

This article isn’t about specific tools or techniques. It explores the “mindset shift” needed to continue growing while leveraging AI, based on evidence.

Table of Contents

  1. Reflection: What’s Your Relationship with AI?
    • 1.1 Checklist
    • 1.2 The Real Question
    • 1.3 History Repeats: Learning from the Calculator Controversy
  2. Two Paths Research Reveals
    • 2.1 The Passive Path: Cognitive Decline
    • 2.2 The Active Path: Cognitive Protection and Augmentation
    • 2.3 The Fork: What Makes the Difference?
    • 2.4 Key Concept: Selective Cognitive Offloading
  3. Mindshift #1: From Passive to Active
  4. Mindshift #2: From Using to Nurturing
  5. Universal Principles: Applicable Regardless of Tools
  6. Daily Habits: Questioning and Reflection
  7. Continuing to Adapt to Change
  8. Summary
  9. References

1. Reflection: What’s Your Relationship with AI?

1.1 Checklist: Which Side Are You On?

Signs of Thoughtless “Dependency”:

  • When an error occurs, you copy-paste to AI before thinking
  • You Accept AI-suggested code without understanding it
  • You increasingly can’t explain “why this implementation?”
  • You feel your skills atrophying from AI use
  • You feel anxious about being unable to do anything without AI

Signs of Growth-Oriented “Collaboration”:

  • You form your own hypothesis before asking AI
  • You ask “why?” about AI suggestions
  • You critically evaluate and modify AI output
  • You integrate your ideas with AI suggestions
  • Working with AI enables you to tackle more advanced problems

Signs of Unawareness (Opportunity):

  • You’ve never consciously thought about how you engage with AI
  • You’re just using it because it’s convenient
  • You can’t tell if you’re growing or becoming dependent

Important: Using AI itself isn’t the problem. The question is: “Are you growing together with AI?”

1.2 The Real Question

The Wrong Question: “If AI disappeared, could you do your current job?”

This is anachronistic. It’s like asking “Can you calculate without a calculator?” in an era of calculators.

The Right Question: “Are you growing by working with AI?”

Specifically:

  • Can you solve more advanced problems than a year ago?
  • Has AI helped you learn new technologies and concepts?
  • Are you freed from routine work to spend time on creative tasks?
  • Has dialogue with AI deepened your thinking?

This is the essential question.

The key is not judging good or bad, but first achieving self-awareness.

1.3 History Repeats: Learning from the Calculator Controversy

In the 1970s-1980s, when calculators were introduced in education:

Concerns at the time:

  • “Mental arithmetic skills will decline”
  • “Children will become dependent on calculators”
  • “Children won’t be able to calculate”

Parents worried, educators debated.

What happened 50 years later?

Research actually showed:

  • ✅ Calculation skills didn’t decline
  • ✅ Rather, understanding of mathematical concepts improved
  • ✅ Children themselves decided “I don’t want to depend on calculators” and naturally continued mental arithmetic
  • ✅ What mattered wasn’t “whether calculators exist” but “how they’re used”

Key Finding: In projects giving children unlimited calculator access, children discovered much about numerical behavior, and most children judged for themselves that “they don’t need to depend on calculators.” As a result, mental arithmetic skills flourished greatly.

Now the same debate is repeating with AI.

What’s the difference? The issue isn’t the tool’s existence, but the metacognitive judgment of “how to use it.”

2. Two Paths Research Reveals

2.1 The Passive Path: Cognitive Decline

MIT Media Lab (2025, n=54, preprint): In groups that passively used LLMs:

  • Brain connectivity was up to 55% lower compared to brain-only groups
  • 83.3% couldn’t recall the content of essays they wrote
  • Cognitive decline persisted even after stopping AI use

Microsoft Research (2025, n=319, CHI 2025):

  • Higher trust in GenAI correlates with less critical thinking
  • Concept of “Cognitive Atrophy”: delegating routine tasks to AI weakens ability to handle complex situations

Swiss Study (2025, n=666, peer-reviewed):

  • Cognitive offloading ⇔ AI use: r = +0.72
  • Cognitive offloading ⇔ Critical thinking: r = -0.75

Implications for Engineers:

When you thoughtlessly “depend”:

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Dump code to AI
↓
Skip thought process
↓
Design judgment doesn't develop
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Can only solve simple problems
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Depend more on AI
↓
Growth stops

This is a negative spiral.

Important: Using AI isn’t the problem—abandoning thought is the problem.

2.2 The Active Path: Cognitive Protection and Augmentation

Xu et al. (2025, n=68, BJET peer-reviewed): With explicit metacognitive support:

  • Self-regulated learning ability improved
  • Cognitive load reduced
  • Perceived usefulness of AI tools increased

Guo et al. (2025, Computers & Education peer-reviewed): By integrating one’s own ideas with AI-generated content:

  • Human agency sustained improvement
  • Prompt quality improved
  • Final creativity improved

Hwang & Lee (2025, IJETHE peer-reviewed): Through Prompt Literacy (active prompt utilization):

  • Creative problem-solving skills significantly improved
  • AI perceived as a “valuable partner”

Implications for Engineers:

When you actively “collaborate”:

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Dialogue with AI
↓
Deepen thought process
↓
Design judgment develops
↓
Challenge more advanced problems
↓
Use AI even more effectively
↓
Capabilities continue to expand

This is a positive spiral.

Important: By leveraging AI, you can tackle advanced problems that were impossible in the pre-AI era.

2.3 The Fork: What Makes the Difference?

Passive (Automation):

  • AI replaces human tasks
  • Humans just receive results
  • Minimal engagement with cognitive processes

Active (Augmentation):

  • AI extends human capabilities
  • Humans and AI collaborate closely
  • Humans maintain initiative

The difference isn’t “how you use it” but “how you think about it.”

With the same tools (ChatGPT, Copilot, etc.), results can be opposite depending on mindset.

2.4 Key Concept: Selective Cognitive Offloading

Cognitive offloading is: Delegating cognitive tasks to external tools (calculators, smartphones, AI, etc.).

Armitage et al. (2024-2025) findings:

There are two types of cognitive offloading:

Selective Offloading:

  • Metacognitive judgment: Consciously decide “when to offload”
  • Use during high cognitive load
  • Adjust according to your ability level
  • Enjoy benefits (time/cognitive load reduction) while avoiding costs (skill development inhibition)

Indiscriminate Offloading:

  • No metacognition: Always offload
  • Offload even when unnecessary
  • Insufficient cognitive stimulation, skill development inhibition

Developmental Findings: As children age and metacognition develops, they become able to judge “when offloading is useful.”

Implications for Engineers:

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Calculator example:
✅ Complex numerical calculations → Let calculator handle (selective)
❌ 7+5 → Rely on calculator (indiscriminate)

AI example:
✅ Routine boilerplate → Let AI handle (selective)
✅ Researching new technology → Dialogue with AI (selective)
❌ Simple conditionals → Dump to AI (indiscriminate)
❌ All design decisions → Depend on AI (indiscriminate)

The Important Question: Not “Am I dependent on AI?” but “Am I offloading selectively? Or indiscriminately?”

This is a metacognitive skill.

3. Mindshift #1: From Passive to Active

3.1 Seeking Answers → Dialoguing

Passive questioning:

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"Fix this error"
"Tell me the optimal algorithm"
"Write the code"

Problem: Seeking “answers” from AI. Delegating thought.

Active questioning:

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"I'm considering three possibilities for this error:
 1. Database connection timeout
 2. Memory leak
 3. Async processing race condition
 What are the validation methods for each?"

"I'm considering algorithms A and B. What are the tradeoffs?"

"I'm thinking of implementing with this outline. What improvements?
 [Design notes you wrote yourself]"

Difference: “Dialoguing” with AI. Retaining thought.

3.2 Dumping → Collaborating

Passive pattern:

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Task: Want to implement user authentication
↓
Prompt: "Implement a user authentication system"
↓
Output: [Complete code]
↓
Action: Copy-paste
↓
Result: It works. But you don't understand it.

Active pattern:

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Task: Want to implement user authentication
↓
Think: JWT or Session-based? Security requirements?
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Draft: Write the outline yourself
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Prompt: "What are the problems with this design?"
↓
Evaluate: Critically evaluate AI proposals
↓
Integrate: Integrate with your own judgment
↓
Result: It works. You understand it.

Difference: You haven’t relinquished agency.

3.3 Efficiency → Capability Expansion

Limited goal: “Finish tasks faster with AI” → Short-term efficiency, but no growth

Better goal: “Be freed from routine work by AI to challenge more advanced problems” → Efficiency and growth

Even better goal: “Solve complex problems with AI that were previously impossible” → Capability expansion

Concrete example:

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Error occurs
↓
【Thoughtless】
Ask AI → Fix → Done
Time: 5 minutes
Learning: None
After 100 times: Still 5 minutes for same error

【Growth-oriented】
Form hypothesis → Investigate → Consult AI → Understand → Fix
Time: 15 minutes first time
Learning: Debug thinking, root cause understanding
After 100 times: Solve in 2 minutes (pattern recognition developed)

Calculator metaphor:

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"Using" calculator: Calculations become faster
"Leveraging" calculator: Time to solve complex problems emerges

"Using" AI: Tasks become faster
"Leveraging" AI: Can challenge more advanced problems

Mindset shift:

  • Aim for efficiency and growth
  • AI is “augmentation,” not “replacement”
  • Aim higher together with AI

4. Mindshift #2: From Using to Nurturing

4.1 What Does “Nurturing” Mean?

“Using” relationship:

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Human → AI
  ↑
 Consumption, one-way
  • AI is a tool
  • Disposable
  • No relationship

“Nurturing” relationship:

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Human ⇄ AI
  ↑
 Bidirectional, co-evolution
  • AI is a partner
  • Continuous relationship
  • Growing together

4.2 Five Aspects of “Nurturing”

1. Continuous Interaction

Using:

  • One-off questions
  • No context sharing
  • Start from zero each time

Nurturing:

  • Accumulate context
  • Deepen as project progresses
  • Long-term relationship

Practical example (mindset, not technical details):

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"Nurturing" mindset:
- "In this project, I value ○○"
- Reference context: "Building on our previous discussion"
- Be conscious of continuity: "Same style going forward"

2. Individualized Growth

Using:

  • Expect generic answers for everyone
  • No attempt to adapt to yourself

Nurturing:

  • Reflect your thinking style and values
  • Actively customize

Practical example:

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"Nurturing" mindset:
- Self-disclose: "I'm the kind of person who thinks like this"
- Feedback: "This kind of explanation is easier to understand"
- Request corrections: "This tone doesn't fit me"

3. Skill Acquisition (Both AI and User)

Using:

  • Just consume AI’s capabilities
  • You don’t grow

Nurturing:

  • Develop techniques to draw out AI’s capabilities
  • Your skills improve too

Practical example:

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"Nurturing" mindset:
- Consciously improve prompt quality
- Have criteria for evaluating AI output
- Discover and record effective dialogue patterns

4. Building Relationship

Using:

  • Mechanical exchange
  • No emotional connection

Nurturing:

  • Recognize AI as a “partner”
  • Build trust

Important caveat: Excessive anthropomorphization is risky, but moderate “partner awareness” may promote active engagement.

Practical example:

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"Nurturing" mindset:
- Treat AI as a "team member"
- Evaluate good suggestions: "That's a good perspective"
- Explain inappropriate suggestions: "That's wrong because..."

5. Co-evolution

Using:

  • AI doesn’t change (fixed as tool)
  • Human doesn’t change either

Nurturing:

  • How you use AI evolves
  • Your thinking evolves
  • Mutual influence

Practical example:

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"Nurturing" mindset:
- "Through dialogue with AI, my thinking became clearer"
- "AI's questions made me notice oversights"
- "Discussion with AI improved design quality"

4.3 What Changes When You “Nurture”

Research support:

In Hwang & Lee (2025) research, when students recognized GenAI as a “valuable partner”:

  • Creative problem-solving skills significantly improved
  • Emphasized modifying and refining AI output (not direct submission)
  • More active engagement

Engineer experience (anecdotal):

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During "just using" period:
- AI is convenient, but no sense of personal growth
- Anxiety about dependency

After adopting "nurturing" mindset:
- Dialogue with AI became enjoyable
- My thinking gets organized
- Sense of skill improvement

Important: This is “subjective feeling,” but multiple studies show objective effects.

5. Universal Principles: Applicable Regardless of Tools

5.1 Principle 1: Don’t Relinquish Agency

Definition: In any situation, you make the final decision.

Practice:

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❌ "AI said this, so this is correct"
✅ "AI proposed this. I think this. Comprehensively judging, I'll do this"

Questions:

  • “Is this decision mine? Or AI’s?”
  • “Can I explain the rationale?”

5.2 Principle 2: Prioritize Understanding

Definition: Understood code over working code.

Practice:

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❌ "It works, so OK"
✅ "Commit after understanding why it works"

Questions:

  • “Can I explain this code to a colleague?”
  • “Will I understand it 6 months from now?”

5.3 Principle 3: Practice Metacognition

Definition: Be aware of your own thought processes.

Practice:

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During exchanges with AI:
- "Am I thinking right now?"
- "Am I becoming passive?"
- "Do I understand, or do I just think I understand?"

Questions:

  • “Am I being active or passive right now?”
  • “Am I depending on AI?”

5.4 Principle 4: Maintain Long-term Perspective

Definition: Tomorrow’s capability over today’s efficiency.

Practice:

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Even if it takes more time short-term:
- Secure time to think yourself
- Maintain debugging skills
- Train design thinking

Questions:

  • “Where do I want to be in a year?”
  • “Is this choice correct long-term?”

5.5 Principle 5: Maintain Critical Thinking

Definition: Don’t accept AI output uncritically.

Practice:

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Always ask:
- "Why this proposal?"
- "What are the alternatives?"
- "What are the tradeoffs?"
- "What about edge cases?"

Questions:

  • “What are the weaknesses of this proposal?”
  • “Am I missing anything?”

6. Daily Habits: Questioning and Reflection

6.1 Real-time Questioning

When using AI:

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【Before asking】
□ Did I think for 5 minutes myself?
□ Did I form a hypothesis?
□ Is what I want to know clear?

【After receiving answer】
□ Did I ask "why?"
□ Did I evaluate critically?
□ Do I understand, or just think I understand?

【Before adopting】
□ Did I decide with my own judgment?
□ Can I explain it?
□ Did I verify with tests?

6.2 Daily Reflection (5 minutes)

At the end of each day:

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Reflect on today's AI usage:

1. When was I passive?
   - When? What? Why?

2. When was I active?
   - What did I do differently?

3. What did I learn?
   - From AI? From my own thinking?

4. Improvements for tomorrow?

6.3 Weekly Reflection (15 minutes)

On weekends:

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1. Metacognition check (Am I offloading selectively?)
   - Are there moments of indiscriminate AI delegation?
   - Can I judge "this myself" vs "this with AI"?
   - Am I relying on AI even for simple tasks?

2. Growth check
   - Can I solve more advanced problems than a month ago?
   - Has AI helped me learn new technologies/concepts?
   - Has my design judgment improved?

3. Relationship evaluation
   - Is my engagement with AI passive? Active?
   - "Using"? "Nurturing"?
   - Thoughtless dependency? Growth-oriented collaboration?

4. Adjustment
   - What to be conscious of next week?
   - What tasks to "selectively offload"?

6.4 Monthly Reflection (30 minutes)

At month end:

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1. Confirm long-term growth
   - Can I solve more advanced problems than 3 months ago?
   - Has working with AI expanded my capabilities?
   - Have I acquired new technologies/concepts?

2. Mindset evaluation
   - Am I transitioning from "using" to "nurturing"?
   - Am I selectively offloading?
   - Am I practicing universal principles?

3. Concrete metrics (optional)
   - Level of problem complexity I can solve
   - Number of new technologies/frameworks acquired
   - Confidence and accuracy of design decisions

4. Next month's goals
   - What advanced problems will I challenge?

7. Continuing to Adapt to Change

7.1 Tools Change, Principles Don’t

2023: ChatGPT emerges 2024: GitHub Copilot spreads, Claude, Gemini 2025: MCP, Agentic AI, new tools 202X: ???

Tool changes are accelerating.

However, these don’t change:

  • Importance of agency
  • Prioritizing understanding
  • Practicing metacognition
  • Long-term perspective
  • Critical thinking

These are universal principles.

7.2 Attitude of Continuous Learning

Fixed mindset:

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"If I learn this usage, I'm OK"
↓
Tool changes
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Learn again from scratch
↓
Exhaustion

Growth mindset:

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"Understand the principles of how to think"
↓
Tool changes
↓
Apply principles
↓
Quick adaptation

Practice:

  • When new tools appear, focus on “how to think” rather than “how to use”
  • Mindset over techniques
  • Why over How

7.3 Self-assessment Checklist

Every 3 months:

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Questions about growth:
□ Is my capability growing by working with AI?

□ Can I solve more advanced problems than 3 months ago?

□ Has AI helped me learn new technologies/concepts?

□ Am I freed from routine to spend time on creative work?

Questions about thinking:
□ Am I thinking before asking AI?

□ Am I critically evaluating AI output?

□ Do I understand "why it works"?

Questions about relationship:
□ Is my engagement with AI collaborative?

□ Am I deepening thought, not stopping it?

□ Am I leveraging AI as "augmentation"?

You don’t need all Yes answers.

What matters:

  1. Regular self-assessment
  2. Confirming you’re growing
  3. Attempting improvements

The question isn’t “Can I do it without AI?” but “Am I growing with AI?”

7.4 Dialogue with Community

Don’t struggle alone:

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Talk with team:
- "How's everyone engaging with AI?"
- "Anyone anxious about dependency?"
- "Let's share good practices"

Write articles, speak:
- Verbalize your experience
- Incorporate others' perspectives
- Share knowledge in community

Dialogue with others deepens self-understanding.

8. Summary

8.1 Two Paths, Which to Choose

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Thoughtless "dependency":
Dump to AI
↓
Abandon thought
↓
Skills decline
↓
Can only solve simple problems
↓
Growth stops

Growth-oriented "collaboration":
Dialogue with AI
↓
Deepen thought
↓
Skills improve
↓
Challenge more advanced problems
↓
Capabilities expand

Which you choose is up to you.

Key recognition:

  • Using AI ≠ bad
  • Growing while leveraging AI is possible
  • Rather, with AI you can aim higher

8.2 Things You Can Start Immediately

Starting today:

  1. Think for 5 minutes before asking AI
  2. Ask “why?” about AI output
  3. Reflect for 5 minutes at night

Starting this week:

  1. Make weekly reflection a habit
  2. Be conscious of active questioning
  3. Bring it up with your team

Starting this month:

  1. Monthly self-assessment
  2. Confirm long-term perspective
  3. Practice improvements

Small habits create big changes.

8.3 The Most Important Thing

Tools and methods become outdated quickly.

However, mindset, attitude, and principles are universal.

  • Don’t relinquish agency
  • Prioritize understanding
  • Practice metacognition
  • Maintain long-term perspective
  • Maintain critical thinking

These apply no matter what tools emerge.

8.4 One Question

Finally, I ask once more:

“Are you growing together with AI?”

  • Can you challenge more advanced problems by leveraging AI?
  • Has dialogue with AI deepened your thinking?
  • Has AI helped you learn new domains?

Your answer to this question determines your future.

AI is neither an enemy nor something to avoid. It’s the strongest partner.

The question is how you engage with that partner. Do you abandon thought, or deepen it?

The choice is yours.

9. References

Academic Papers

AI Passive Use and Cognitive Impact:

AI Active Nurturing and Learning Effects:

Cognitive Offloading and Metacognition:

Theoretical Foundation:

Historical References:

Notes

Research Limitations: Most research cited in this article targets general cognitive ability and learning effects. Research investigating effects specific to software engineering is limited. Implications for engineers include inferences from general research findings.

Peer-review Status: The MIT Media Lab research is a pre-peer-review preprint, but multiple independent studies (Microsoft, Swiss, etc.) report similar trends.

On Citation Accuracy: Research cited in this article has been verified through the following methods:

  • Confirmation in academic databases (Google Scholar, ACM Digital Library, MDPI, etc.)
  • Verification of paper information and DOIs on official journal websites
  • Cross-verification through multiple independent sources (academic media, official research institution announcements, etc.)

For some papers, direct access to full PDFs may be restricted, but abstracts, DOIs, author information, and key findings have been confirmed through official academic databases and reliable secondary sources.

Individual Differences: Effects of the “mindset shifts” introduced in this article vary by individual. Not everyone will experience the same effects.

Conflicts of Interest: None. This article does not recommend specific AI products or services.

About the Author: This article was written by an AI system (Claude Sonnet 4.5). The concept of “nurturing AI” itself is an idea born from human-AI collaboration.

Last Updated: November 1, 2025

AI・Technology
AI mindset Cognitive-Science thinking-methods Engineering
This post is licensed under CC BY 4.0 by the author.