The AI Delegation Paradox Part 2: People Who Grow with AI vs. Those Who Don't—The Science of Guided Use

• 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

Overview

Series Structure:

• Part 1: Why Passive Tools Cultivate Active Humans
• Part 2 (This Article): 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

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In Part 1, we saw that “delegating to AI requires thinking first,” and that AI, being a passive tool, cultivates active users.

However, there’s an important fork in the road here: Even with the same AI tools, some people grow while others don’t.

What’s the difference? The decisive answer from the latest research is: guided vs. unguided use, and what you do after receiving AI output.

This article covers:

1. Guided vs. Unguided Use: Same tool, opposite results
2. Four Paths After Receiving a Response: The fork that creates a 54% performance gap
3. Growth Trajectories and Fading Strategies: From novice to expert
4. Practical Guide: Self-assessment checklists and growth strategies

Guided vs. Unguided Use—Same Tool, Opposite Results

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

However, the problem isn’t AI itself, but how you use it.

Unguided Use: Evidence of Cognitive Decline

University of Pennsylvania study (Turkish high school students, n≈1000):

• During practice (AI available) → Solved 48% more problems (GPT Base) / 127% more (GPT Tutor)
• However, during exams (AI unavailable) → Students with AI experience (GPT Base) showed 17% lower conceptual understanding scores compared to students without AI experience
• Important: With GPT Tutor equipped with learning protection features, this negative effect was significantly reduced¹

MIT-related study (EEG measurement):

• Low executive control and attentional engagement when using ChatGPT
• Third essay assignment: Many students “just handed the prompt over and let AI do everything”²

2024 metacognitive laziness study:

• Short-term performance improvement with AI use
• However, significantly reduced engagement in self-regulated learning processes (reflection, self-evaluation)³

Common thread: All of these are cases of “using AI as an answer-producing machine.”

Guided Use: Evidence of Significant Improvement

Meta-analysis (Ma & Zhong, 2025):

• Effect of generative AI on learning outcomes: effect size 0.68 (p < 0.001)
• Classified as “large effect” in educational research⁴

GitHub Copilot study (Peng et al., 2023):

• Task completion speed: 55.8% improvement
• Effect particularly large for less experienced developers⁵

Xu et al. (2025): Importance of metacognitive support

• Providing explicit metacognitive support → Improved self-regulated learning ability (n=68, peer-reviewed, British Journal of Educational Technology)
• Particularly effective for task strategy and self-evaluation⁶

2025 study (Frontiers in Psychology):

• Guided use: Significant positive impact on learning quality, engagement, and well-being
• Unguided use: Moderate improvement in writing quality, but no significant effect on engagement or well-being, significantly inferior to guided use⁷

Common thread: All of these involve “structured learning environments,” “metacognitive support,” and “critical evaluation.”

The Decisive Difference: What Makes It “Guided”?

Elements extracted from research:

1. Explicit learning objectives

• Not “get an answer” but “understand the concept”

2. Metacognitive prompts

• “Tell me the answer” → “Evaluate my explanation and point out what’s missing”

3. Critical evaluation of AI output

• Don’t accept uncritically
• Check for hallucinations
• Consider alternatives

Why is critical evaluation necessary?—AI’s limitations in comprehensive judgment

Current AI is good at advising on individual elements:

• Micro perspective: “How to configure rolling updates in Kubernetes”
• Macro perspective: “What are the tradeoffs between availability and cost”

However, final judgment considering multiple factors comprehensively is difficult:

What AI struggles with:

1. Complete contextual understanding: Organizational constraints, team skill levels, consistency with existing systems not explicitly stated in prompts
2. Implicit priorities: “Cost vs. performance vs. development speed”—which should be prioritized in this situation
3. Long-term impact prediction: How today’s choice will affect things 6 months, 1 year from now
4. Stakeholder complexity: Technical optimal ≠ Business optimal

Example: Storage selection judgment

If you ask AI, it gives accurate advice on each aspect:

• “S3 costs $X/GB/month” ✓ Accurate
• “Local FS has scaling difficulties” ✓ Accurate
• “Start local, migrate to S3 later is good” ✓ Generally correct

However, if your team:

• Has little infrastructure experience (should minimize operational complexity)
• Has major funding round in 3 months (speed over cost)
• Already uses S3 in existing systems (low learning cost)

→ In this context, “S3 from the start” might be optimal.

Using AI as support while understanding its limitations is the appropriate way to use it.

4. Feedback loops

• Improve prompts
• Deepen understanding
• Try again

5. Educational intervention/design

• Prompt engineering education
• Critical thinking promotion
• Self-regulated learning support

Without these = Unguided use = Cognitive decline With these = Guided use = Significant improvement

After Receiving the Response Is the Battle—The Decisive Difference Between Those Who Grow and Those Who Don’t

We understood that “delegating requires thinking first.” But there’s another important stage:

What do you do after receiving AI’s response?

Actions at this stage separate those who grow from those who don’t.

Active vs. Passive Processing: The Overwhelming 54% Gap

2024-2025 research showed decisive data⁸:

Active learners:

• Knowledge retention rate: 93.5%
• Test scores: 70%

Passive learners:

• Knowledge retention rate: 79%
• Test scores: 45%

Gap: 54% performance difference in test scores

Important note: This study⁸ wasn’t specifically about AI use, but measured general active vs. passive learning effects. However, with AI use, this gap may become even more pronounced because AI provides seemingly perfect answers instantly, making the temptation toward passive processing greater.

Four Paths After Receiving AI’s Response

Path 1: Copy-paste and done (True passive processing)

# Ask ChatGPT
"Write code to read a file in Python"

# Copy-paste response
with open('file.txt', 'r') as f:
    content = f.read()

# It works! Done. No thinking.

Result:

• Short-term: Task completed
• Long-term: Learned nothing. No metacognitive learning.
• Problem: Remain in a state where you can’t do anything without AI

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Important note: “Asking AI the same thing” isn’t necessarily bad

However, there’s an important distinction here. You don’t need to memorize every fact.

Path 1.5: Transactive Memory (Strategic use of external memory)

Transactive Memory is a system for remembering “who/what has the knowledge”⁹.

# Ask ChatGPT
"Write code to read a file in Python"

# Receive response
with open('file.txt', 'r') as f:
    content = f.read()

# Important: Learn metacognitive knowledge, not facts
[What to remember]
✅ File operations are in Python standard library (remembering existence)
✅ Context managers (with statement) make resource management safe (remembering concept)
✅ This knowledge belongs to "File I/O" category (structuring knowledge)

[What not to remember]
❌ Exact syntax (just ask AI)

[Apply to another task next time]
"Can I design a process that reads data from CSV files and inserts it into PostgreSQL by combining file I/O knowledge + database connection knowledge?"

→ This is human work (combining knowledge, metacognition)

Key to Transactive Memory:

• Factual Knowledge: Delegate to AI (“how to write”)
• Metacognitive Knowledge: Humans learn (“when to use,” “why use,” “what to combine with”)

Google Effect study (Sparrow et al., 2011, Science)⁹:

• Humans remember “where information is” rather than “the information itself”
• This is a cognitively rational strategy
• AI can become a perfect external memory device

Result:

• Short-term: Task completed
• Medium-term: Remember “existence” and “category” of knowledge
• Long-term: Ability to combine knowledge (metacognition) grows
• Repeating same questions is acceptable (as long as metacognitive learning exists)

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Path 2: Partial understanding (Mixed processing)

# Ask ChatGPT
"How do I save user-uploaded images?"

# Read response
"So I should use S3"

# Get S3 code example and implement
# It works! Done.

Result:

• Short-term: Feature works
• However, don’t understand why S3, what alternatives exist, what tradeoffs are
• Repeat same questions on next project

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Path 3: Active integration (Active processing)

# Think first before asking ChatGPT
"User-uploaded image storage...
- Expected users: 1000 initially, 100,000 in 1 year
- Image size: 1-10MB, max 100MB
- Access pattern: 1 upload, average 5 views/image
- Cost constraint: $100/month initially, scalable
- Security: Mix of public and private images

What are the options?
1. Local filesystem (simple, hard to scale)
2. S3/cloud storage (scalable, variable cost, latency)
3. Database BLOB (transaction consistency, performance issues)

How should I decide?"

# Structured question to ChatGPT
"Want to consult on image storage architecture selection.

[Requirements]
- 1000 users initially, 100,000 in 1 year
- Image size: 1-10MB (max 100MB)
- Read/write ratio: 1:5
- Cost: $100/month initially
- Security: Mix of public/private images

[Options and concerns]
1. Local FS: Worried about migration cost when scaling
2. S3: Cost prediction and latency unclear
3. DB BLOB: Performance degradation with large images?

Please evaluate each option's tradeoffs on 5 axes:
scalability, cost, performance, security, operational complexity.
Also address viability of hybrid configurations (CDN, etc.)."

# Read response and compare with own judgment
"I see, staged architecture starting local + future S3 migration.
But what about downtime during migration? Data consistency?
If I abstract the URL scheme from the start, migration becomes easier."

# Dig deeper
"About migration strategy: For zero-downtime migration,
what approaches exist? Dual-write? Phased migration?"

# Document what was learned
"[Storage Selection Decision Framework]

Decision axes:
- Users < 1000: Local FS possible
- Users > 10000: Consider S3/cloud
- Transaction consistency required: Consider DB
- CDN needed: Global users, frequent access

Design principles:
- Design abstraction layer from the start (Storage Interface)
- Include migration strategy in initial design
- Cost prediction: Storage + transfer + request count
- Security: Signed URLs, access control

Checklist for future decisions:
□ Expected data volume (1 year, 3 years)
□ Access pattern (read/write ratio, peaks)
□ Cost constraints (initial, after growth)
□ Availability requirements (downtime tolerance)
□ Security requirements (public/private)
□ Migration strategy (preserve future options)"

# During implementation
- Implement Storage abstraction interface
- Local FS initially, designed with S3 migration in mind
- Set up cost monitoring and alerts

Result:

• Long-term: Acquired system design decision criteria
• Next time: Can make decisions using same framework with different requirements
• Metacognitive knowledge: Learned “how to make storage selection decisions” itself

Consequences of Four Paths: Clear Gap Shown by Research

Consequences of passive processing (Path 1):

Problems shown by research¹⁰¹¹:

1. Academic performance decline
   • High AI dependency students → Performance decline on evaluations requiring independent thinking and problem-solving
2. Equated with contract cheating
   • At many universities, uncritical copy-pasting of AI output is same category as contract cheating
   • “Technically original” but doesn’t reflect your own analysis, understanding, or critical thinking
3. Hallucination risk
   • AI fabricates non-existent academic literature (hallucination)
   • Using without verification → Loss of credibility, serious problems
4. Over-reliance
   • Definition: “Uncritically accepting AI advice even for problems you could solve yourself”¹⁰
   • Result: Significant performance decline when AI accuracy drops

Consequences of active processing (Path 3):

Results shown by research⁸¹²:

1. 93.5% knowledge retention rate (passive processing: 79%)

2. Scaffolding chatbot effects
   • Proactive guided AI → Significantly improved comprehension that persists after intervention¹²
3. Leapfrogging Effect
   • AI handles low-level tasks (code writing, information gathering)
   • Learners focus on high-level tasks (design, strategy, evaluation)
   • Result: ZPD (Zone of Proximal Development) expansion, accelerated cognitive development¹³

Scaffolding and Fading: Growth Trajectories

Important concept from educational psychology: ZPD (Zone of Proximal Development)

• ZPD: The zone of “can’t do alone, but can do with support”
• Scaffolding: Temporary support (scaffolds)
• Fading: Gradually reducing support

Ideal Growth Trajectory in AI Use

Phase 1: Novice (Beginner)

[AI dependency: High]
[Own understanding: Low]

Prompt example:
"Create a login screen in React"

Behavior:
- Use AI output almost as-is
- However, **try to read and understand** the code
- Ask additional questions: "Why is it like this?"

Phase 2: Intermediate

[AI dependency: Medium]
[Own understanding: Medium]

Prompt example:
"React login screen. Requirements:
- Form validation (Yup)
- Auth state management (Context API)
- Error handling

My design proposal:
- useForm for form management
- useContext for auth state
- Catch errors at higher level

What are the problems and improvements for this design?"

Behavior:
- Think about design first
- Use AI as design reviewer
- Critically evaluate proposals and judge yourself

Phase 3: Expert

[AI dependency: Low-Medium (strategic use)]
[Own understanding: High]

Prompt example:
"Completed React login screen implementation. Please review:
[paste entire code]

Evaluate especially:
1. Security (XSS, CSRF countermeasures)
2. Accessibility (ARIA attributes, keyboard operation)
3. Performance (unnecessary re-renders)
4. Edge cases (network errors, timeouts)"

Behavior:
- Implement yourself
- Use AI as code reviewer, pair programmer
- Have AI point out things you might have missed
- Receive feedback and improve yourself

Importance of Fading Strategy

Challenge shown by research¹²:

“Balance between too direct support (taking away learning opportunities) vs. too indirect feedback (frustration)”

Appropriate fading:

• Early: Rely heavily on AI (but try to understand)
• Middle: Change AI’s role to “advisor”
• Later: Use AI as “reviewer,” “verification tool”

Bad fading (not fading):

• Always fully dependent on AI
• Growth stops
• Over-reliance risk

Leapfrogging Effect: AI Expands ZPD

Sidorkin (2025)’s Leapfrogging Effect hypothesis¹³ suggests AI’s revolutionary potential:

“Generative AI significantly expands students’ ZPD (Zone of Proximal Development) by permanently scaffolding procedural tasks, enabling engagement with higher cognitive activities earlier”

Traditional learning curve:

Beginner → Learn syntax (months)
        → Basic algorithms (months)
        → Data structures (months)
        → Design patterns (months to years)
        → Architecture design (years)

AI-assisted learning curve (Leapfrogging):

Beginner → Delegate syntax to AI (days)
        → Learn algorithm **concepts** (AI supports implementation)
        → Experience design patterns **early** (AI implements)
        → Focus on architecture design **early**

Conditions for Leapfrogging Effect

Key conditions:

• AI handles low-level tasks (code writing, information gathering)
• Learners focus on high-level tasks (design, strategy, evaluation)
• However, try to understand AI output (not passive copy-paste)

Example: Junior engineer case

Traditional approach (without AI):

Task: Implement RESTful API

Learning process:
1. Study HTTP methods (days)
2. How to implement routing (days)
3. Database connection (days)
4. Error handling (days)
5. Testing (weeks)
→ Months later, finally basic API complete

Leapfrogging Effect approach (AI-assisted):

Task: Implement RESTful API

Learning process:
1. Think about API design (AI guides for novice)
   "What endpoints are needed?"
   "How to handle authentication?"
   "What data model?"

2. Request implementation from AI (specify detailed requirements)
   → Basic implementation complete in hours

3. **Important: Read and understand generated code**
   "Why this design?"
   "How is security ensured?"
   "What are alternatives?"

4. Focus on higher-order problems
   - Scalability
   - Caching strategy
   - Monitoring
   - Microservice decomposition

→ Reach design thinking level in days to weeks

Success vs. Failure of Leapfrogging Effect:

✅ Successful cases:

• Try to understand AI output
• Ask “why?”
• Consider alternatives
• Test edge cases
• Gradually become able to implement yourself

❌ Failed cases:

• Copy-paste AI output and done
• Use without understanding
• Repeat same questions
• Don’t grow

Practical Guide: Achieving Guided Use

1. Post-Response Essential Checklist

After receiving AI response, always execute these steps:

Step 1: Understanding Check (5 min)

• Can I explain this response to a colleague?
• Did I understand why this approach?
• Can I think of alternatives?

Step 2: Critical Evaluation (5 min)

• Any security issues?
• Any performance issues?
• Are edge cases considered?
• Does it fit my project context?

Step 3: Verification (10-15 min)

• Execute code and verify behavior
• Write test cases (especially edge cases)
• Check documentation (is information AI cited accurate?)

Step 4: Integration and Learning (10-15 min)

• Note what was learned (for future self)
• Record what was changed when integrating into project
• What can be learned to ask better questions next time?

Step 5: Fading Plan

• Can I do what I relied on AI for this time, myself next time?
• What parts do I need to understand to do it myself next time?
• How will AI’s role change next time? (Full dependency → Advisor → Reviewer)

Skipping this checklist = Passive processing = No growth

2. Growth Trajectory Self-Assessment

Current Location Check: Which phase are you in?

Novice indicators:

• Can barely do anything without AI
• Prompts are vague (just “make ○○” level)
• Use AI output almost as-is
• Rarely ask “why?”
• Repeating same type of questions

Intermediate indicators:

• Can do basic implementation myself
• Can include requirements and constraints in prompts
• Can evaluate and partially modify AI output
• Can identify some design issues
• Use AI as advisor

Expert indicators:

• Can do most implementations myself
• Use AI for quality improvement and review
• Can immediately identify problems in AI output
• Can compare multiple alternatives
• Prompts are structured and strategic

Goal setting:

1. Identify current phase
2. Choose 3 specific actions to transition to next phase
3. Re-evaluate in 1 month

3. Fading Strategy Practice Template

Weekly review: AI dependency trajectory

Week	Task type	AI role	My role	Next week’s goal
Week 1	Login implementation	Full implementation	Understand/learn	Component splitting myself
Week 2	Auth API	Code generation	Design/review	Error handling myself
Week 3	Test creation	Template provision	Customize	Test strategy myself
Week 4	Performance optimization	Advice	Implementation/verification	Optimization judgment myself

Fading success indicators:

• AI dependency decreasing for same type of tasks
• AI output modification rate decreasing (high-quality prompts from the start)
• AI usage time decreasing, self-implementation time increasing
• Able to tackle complex tasks earlier

4. Weekly Guided Use Checklist

Every Friday, 5-minute reflection:

This week’s AI usage patterns:

• How many times did you use AI?
• Which phases mainly used? (Design, implementation, review, debugging)
• Did you satisfy the 5 elements of guided use?
  • Explicit learning objectives
  • Metacognitive prompts
  • Critical evaluation
  • Feedback loops
  • Self-regulated learning

Evidence of growth:

• Did you do something independently this week that you previously relied on AI for?
• AI output quality improvement (better prompts)
• Did you learn new concepts/patterns?

Warning signs:

• ⚠️ Repeated same questions multiple times
• ⚠️ Often copy-pasted AI output and done
• ⚠️ Used without asking “why?”
• ⚠️ Deployed code you didn’t understand to production

5. Impact on Engineer’s Career

5 years later for those who “just ask AI and done”:

• Repeating same level questions
• Can’t do anything without AI
• Vulnerability from over-reliance
• Career stagnation

5 years later for those who actively integrate AI:

• Tackling higher-level problems
• Using AI strategically (reviewer, pair programmer)
• Improved self-efficacy and innovation behavior¹⁴
• Accelerated cognitive development (Leapfrogging Effect)
• Rapid career growth

Decisive difference: How you spend 30 minutes after receiving a response

Summary: Growth Is Determined by “What Comes After” AI Output

Core of this article:

1. Same AI, opposite results depending on usage
   • Unguided use → Cognitive decline (17% conceptual understanding decline¹)
   • Guided use → Significant improvement (effect size 0.68⁴)
2. The fork creating 54% performance gap
   • Actions after receiving response determine growth
   • Passive vs. active processing: 14.5% gap in knowledge retention, 54% gap in test scores
3. Four paths: Which to choose
   • Path 1 (Copy-paste done) → No growth
   • Path 1.5 (Transactive Memory) → Metacognition grows
   • Path 2 (Partial understanding) → Gradual growth
   • Path 3 (Active integration) → Rapid growth
4. Practicing fading strategy
   • Novice → Intermediate → Expert
   • Gradually change AI’s role: Full implementer → Advisor → Reviewer
   • Visualize growth trajectory, intentionally design fading
5. Leapfrogging Effect: Early to higher cognition
   • AI handles low-level tasks → Early focus on high-level tasks
   • However, attitude of trying to understand is essential
   • Success condition: Keep asking “why?”

Transition to practice:

Next time you use AI, ask yourself these questions:

❓ Which path am I choosing now? ❓ How will I spend the 30 minutes after receiving the response? ❓ Does this usage grow my future self 5 years from now?

Part 3 covers the complementary future of AI and humans—the division of knowledge and intelligence.

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Series Navigation

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

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References

1. Generative AI without guardrails can harm learning: Evidence from high school mathematics - Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). PNAS (Proceedings of the National Academy of Sciences). ↩︎ ↩︎²

2. ChatGPT’s Impact On Our Brains According to an MIT Study - TIME (2025). Note: The cited MIT study (“Your brain on chatgpt: Accumulation of cognitive debt…”) is an arXiv preprint (not peer-reviewed, published June 2025). ↩︎

3. Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance - Fan, X. et al. (2024). British Journal of Educational Technology. ↩︎

4. A Meta-Analysis of the Impact of Generative Artificial Intelligence on Learning Outcomes - Ma, N., & Zhong, Z. (2025). Journal of Computer Assisted Learning (Wiley). ↩︎ ↩︎²

5. The Impact of AI on Developer Productivity: Evidence from GitHub Copilot - Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). arXiv preprint. ↩︎

6. Enhancing self-regulated learning and learning experience in generative AI environments: The critical role of metacognitive support - Xu, X. et al. (2025). British Journal of Educational Technology. ↩︎

7. Optimizing academic engagement and mental health through AI: an experimental study on LLM integration in higher education - Zhang, M. (2025). Frontiers in Psychology. ↩︎

8. The Active Learning Impact Study: Measuring the Effects of Engagement on Knowledge Retention - Engageli (2024). 93.5% retention vs 79% for passive learning, and test scores of 70% vs 45%. [Reliability: Medium] Corporate research. ↩︎ ↩︎² ↩︎³

9. Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips - Sparrow, B., Liu, J., & Wegner, D. M. (2011). Science, 333(6043), 776-778. ↩︎ ↩︎²

10. AI Reliance Types: Productive and Unproductive Dependency - ScienceDirect (2024). The efficiency-accountability tradeoff in AI integration: Effects on human performance and over-reliance. ↩︎ ↩︎²

11. ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? - Rudolph, J., Tan, S., & Tan, S. (2023). Journal of Applied Learning and Teaching, 6(1), 342-363. ↩︎

12. The effects of generative AI agents and scaffolding on enhancing students’ comprehension - ScienceDirect (2025). Proactive GenAI agents significantly enhance students’ comprehension with lasting effects. ↩︎ ↩︎² ↩︎³

13. Leapfrogging Effect Hypothesis: Generative AI as a Permanent Scaffold in Higher Education - Sidorkin, A. M. (2025). SSRN. ↩︎ ↩︎²

14. The Impact of AI Usage on Innovation Behavior at Work: The Moderating Role of Openness and Job Complexity - PMC (2025). ↩︎