The Feynman Technique in UNOP: Master Complex Learning Through Teaching
- Martin Swartz
- Sep 8, 2025
- 17 min read
Updated: Jan 15
Richard Feynman (1918–1988) was an American theoretical physicist, Nobel Prize in Physics laureate (1965), renowned for his work in quantum electrodynamics and his exceptional, innovative teaching.
Discover how Nobel laureate Richard Feynman revolutionized learning by rejecting memorization. Learn the four-step technique that doubles academic performance, and explore how University 365 implements this science-backed method through video creation and AI role-play simulations to transform how fellows teach and learn.
INTRODUCTION
In the 1960s, a young physicist named Richard Feynman faced a crisis at Caltech (California Institute of Technology). The freshman physics curriculum was broken. Students were memorizing formulas without understanding the underlying principles, much like parrots repeating sounds without meaning. Feynman knew there had to be a better way.
What emerged from his frustration became one of education's most powerful learning frameworks: a technique that rejects rote memorization in favor of genuine comprehension. Today, this method is transforming how we approach complex subjects, from physics to language learning to leadership development.
But here's what's remarkable: the Feynman Technique isn't just effective for passive learners. When integrated into modern pedagogy, it becomes a catalyst for something far more powerful. U365 (University 365) has recognized this potential and built it into the DNA of its learning platform, particularly through its UNOP methodology. By combining Feynman's principles with cutting-edge modalities—pedagogical video creation and AI-powered role-play simulations—U365 is redefining what's possible in personalized, outcome-driven education.
This lecture explores the science, the method, and the future of learning through teaching.
WHY IT'S IMPORTANT
Target Audience
Fellows and educators pursuing deep mastery, corporate professionals developing teaching skills, and learners frustrated with traditional methods that prioritize memorization over understanding.
Practical Benefits
Doubles comprehension and retention compared to traditional study methods
Builds teaching confidence through structured practice and feedback
Accelerates skill transfer by forcing you to identify knowledge gaps early
Scales learning across organizations through AI-assisted coaching
Develops metacognitive awareness (thinking about your thinking) for lifelong learning
Clear Learning Expectations
By the end of this lecture, you will understand:
The four core steps of the Feynman Technique
The neuroscience explaining why it works so effectively
How U365 implements this method through two innovative modalities
Real-world evidence of its impact on academic performance
How to apply this immediately in your own learning journey
OVERVIEW: 5 Key Takeaways
1. The Feynman Technique is a metacognitive framework, not a study hack.
It works by forcing learners to identify and fill knowledge gaps systematically, activating brain regions responsible for deep comprehension and long-term memory retrieval.
2. Teaching others (or teaching to imaginary others) is the ultimate learning accelerator.
The act of explaining forces simplification, which reveals conceptual holes immediately. Students who use Feynman-based teaching report 17% improvement in language proficiency and 100% improvement in comprehension compared to control groups.
3. U365's two-modality implementation creates a feedback loop that amplifies learning.
By requiring fellows to create pedagogical videos AND practice teaching through AI simulations, U365 ensures both reflection and real-time performance coaching.
4. The science is rock-solid: fMRI studies show Feynman activates deep memory systems.
Research confirms it improves academic self-efficacy, metacognitive strategies, and knowledge retention by 50-75% longer than traditional classroom learning.
5. Self-evaluation paired with expert feedback creates the optimal learning environment.
When fellows self-assess their video explanations and receive personalized coaching from U.Coaches, they develop both critical self-awareness and trust in external feedback mechanisms.
THE STORY OF RICHARD FEYNMAN
AND THE BIRTH OF A TECHNIQUE
Who Was Richard Feynman?
Richard Phillips Feynman (1918-1988) was a Nobel Prize-winning physicist who fundamentally changed how we understand quantum mechanics and electrodynamics. But what made him truly exceptional wasn't just his genius, it was his teaching philosophy.
Feynman's father, Melville, profoundly shaped his approach to learning. Rather than stuffing young Richard with facts, his father asked pointed questions: "What don't you know? What don't you understand?" This practice of identifying ignorance became the cornerstone of Feynman's entire learning methodology.
The Pivotal Moment: Brazil, 1951
The technique crystallized during Feynman's sabbatical in Brazil, teaching physics to prospective teachers. He was horrified to discover something alarming: his students could recite scientific definitions perfectly but couldn't apply them to real problems. They had memorized without understanding.
When Feynman asked a student to explain gravity, the answer came back as pure jargon, technically correct but conceptually hollow. This experience drove home a fundamental truth:
Memorization creates the illusion of knowledge;
teaching reveals the reality of understanding.
Feynman's Teaching Revolution at Caltech
In the early 1960s, Caltech's freshman physics program was in crisis. Feynman was invited to redesign it. Over three years, he meticulously reconstructed the entire curriculum, asking himself constantly: "If I had to explain this concept simply, without jargon, could I?"
The result was The Feynman Lectures on Physics, a series of textbooks that became classics precisely because they prioritized conceptual clarity over mathematical complexity. This work codified his learning methodology into the framework we now call the Feynman Technique.
THE FOUR STEPS OF THE FEYNMAN TECHNIQUE
Step 1: Choose a Concept and Study It
Select a specific topic you want to master. Do the initial learning using textbooks, videos, lectures, or any primary source. Don't try to memorize; instead, focus on identifying the key ideas and gathering your baseline understanding.
Example: A fellow wants to master "machine learning algorithms." They watch videos, read papers, lectures and take notes without trying to perfect their understanding.
Step 2: Teach It to a 12-Year-Old (Or an Imaginary Audience)
This is where the magic happens. Take a blank piece of paper and explain the concept as if teaching a child who has no prior knowledge. Use simple language. Avoid jargon. Speak it aloud as you write.
If you get stuck, that sticking point is pure gold, it's a gap in your understanding you need to fill.
Example: The fellow attempts to explain machine learning without using terms like "neural networks" or "hyperparameters." They quickly realize they can't explain why gradient descent works without deeper understanding.
Step 3: Identify and Fill Knowledge Gaps
Review your explanation. Look for areas where you:
Used technical jargon to cover weak understanding
Got stuck and couldn't proceed
Made logical leaps without clear reasoning
Return to your source material and focus specifically on these gaps. Repeat step 2 until your explanation flows cleanly.
Example: The fellow dives deeper into the mathematics of gradient descent, relearns linear algebra, then attempts the explanation again, this time with genuine clarity.
Step 4: Refine and Test Your Understanding
Review your simplified explanation. Challenge any technical language that crept back in. Try teaching the concept to someone who knows the field, can they understand your explanation? Better yet, can they apply the concept based on your teaching?
Example: The fellow explains machine learning to a colleague, answers their questions effortlessly, and successfully helps them apply the concept to a real project.
WHY THE FEYNMAN TECHNIQUE WORKS:
THE SCIENCE BEHIND IT
Neuroscience: Activating Deep Learning Systems
When you use the Feynman Technique, you're literally activating different brain regions.
fMRI Research Findings: Brain imaging studies show that the Feynman Technique activates neural regions associated with:
Deep comprehension (prefrontal cortex)
Long-term memory encoding (hippocampus)
Metacognitive reflection (anterior cingulate cortex)
By contrast, passive reading or memorization primarily engages short-term working memory systems that forget information within hours.
The Metacognitive Loop
The Feynman Technique creates what cognitive scientists call a "metacognitive loop", you're thinking about your thinking.
When you attempt to teach, you immediately become aware of what you don't understand. This awareness triggers deeper processing. Your brain activates retrieval pathways multiple times, strengthening long-term memory storage.
Why Simplification is So Powerful
Feynman's genius insight was this: complex language often masks incomplete understanding.
When you strip away jargon and try to explain something simply:
You expose logical gaps immediately
You force yourself to find core principles beneath surface details
You create mental models that transfer to new contexts
Research on "elaborative interrogation" shows that explaining why something is true strengthens retention far more than memorizing the what.
The Active Learning Advantage
Traditional lecture-based learning is passive. You listen. Your brain records surface features. But you don't engage in the struggle that builds understanding.
The Feynman Technique demands active output: writing, speaking, teaching. Studies consistently show that active learning strategies improve performance by 55-100% compared to passive listening.
EVIDENCE:
HOW MUCH BETTER IS THE FEYNMAN TECHNIQUE?
Key Research Findings
Science Education (2023): Using the Feynman Technique doubled average performance on comprehension assessments. Pre-test scores averaged 34%; post-test scores averaged 66%, a 97% improvement.
Language Learning (2024): English language learners exposed to Feynman-based techniques showed 17% improvement in language proficiency, with pre-test averages of 65% rising to 82% post-test.
Reading Comprehension: Students using Feynman methods scored 74 points on average (surpassing minimum competency standards), while control groups averaged only 67. The effect size (Cohen's d = 0.87) indicates a large, meaningful difference.
Knowledge Retention: Simulation-based learning (similar to U365's implementation) helps learners retain knowledge 75% longer than traditional classroom methods. VR-simulation learners were 275% more confident applying skills than traditional classroom learners.
Academic Self-Efficacy: Research on "Quantum Learning Models" (which incorporate Feynman principles) shows significant effects on:
Academic self-efficacy (p<0.05)
Metacognitive skills (p<0.05)
Academic performance overall (p<0.01)
Mathematics Learning: Students using the Feynman Technique in mathematics demonstrated:
Deeper conceptual understanding
Higher confidence in problem-solving
Greater ability to apply concepts to new situations
Preference for this method over traditional instruction
The Meta-Analysis Evidence
A meta-analysis of Feynman-based learning methods found an effect size of d=1.051, classified as "large" in educational research. This means the technique produces meaningful, real-world improvements in learning outcomes across multiple contexts.
UNIVERSITY 365'S REVOLUTIONARY IMPLEMENTATION:
THE UNOP METHODOLOGY
University 365 has recognized that the Feynman Technique's power lies not just in its structure, but in how it's implemented with ongoing feedback and reflection. Rather than treating it as a one-time study hack, U365 has embedded it into the UNOP (University Neuro-Optimized Pedagogy) framework through two innovative modalities.
Modality 1: Self-Created Pedagogical Videos
The Practice: U365 fellows learn a topic, then must create a short video in which they personally explain the concept. It's not a video that could be AI-generated. They must record themselves. This is creating an explanation as if teaching a colleague or mentee.
How It Works:
Preparation Phase: Fellow studies the material deeply
Recording Phase: Fellow creates a 5-15 minute video explaining the topic clearly and simply
Self-Evaluation Phase: Fellow watches the video and evaluates their own clarity, logical flow, and completeness
Coach Evaluation Phase: A U.Coach (expert educator) reviews the video and provides structured feedback
Refinement Phase: Fellow addresses gaps and optionally creates an improved version
Benefits: Creating a video forces multiple layers of the Feynman Technique:
Teaching forces clarity: You can't hide behind jargon on camera
Recording creates accountability: You see yourself explaining and become immediately aware of confusions
Self-evaluation builds metacognition: You learn to critique your own teaching before external feedback
Expert feedback closes gaps: U.Coaches identify conceptual holes you missed and guide deeper understanding
The Science Supporting This: Research on self-explanation in video learning shows that when learners create their own explanations while viewing educational content, learning gains increase significantly. Adding a positive pedagogical agent (like a U.Coach) paired with self-generated explanations produces the strongest learning outcomes.
Modality 2: Teaching Fellows to Teach Through AI Role-Play Simulations
The Practice: U365 teaches fellows how to teach using the Feynman Technique. Then, to practice, fellows engage in role-play simulations with U.Copilot, our AI Agent that simulates a learner asking questions, challenging explanations, and responding to teaching approaches.
How It Works:
Instruction Phase: Fellow learns principles of effective teaching based on the Feynman Technique
Simulation Phase: Fellow teaches a topic to U.Copilot (AI in learner role) in a risk-free environment
Adaptive Feedback Phase: U.Copilot responds with confusion, asks clarifying questions, and sometimes misunderstands intentionally to test the fellow's ability to reteach
Immediate Analysis: Fellow receives instant feedback on communication clarity, logical flow, and ability to detect and correct misunderstanding
Iteration Phase: Fellow repeats the scenario, refining their teaching approach
Benefits: AI role-play simulations provide benefits traditional methods cannot:
Scalability: Hundreds of fellows can practice simultaneously without needing human role-players
Consistency: Every simulation provides comparable feedback based on the same criteria
Safety: Fellows fail privately with AI, building confidence before teaching in high-stakes settings
Adaptability: U.Copilot adjusts difficulty and scenarios based on fellow performance
Data-Driven Feedback: AI analyzes voice tone, pacing, word choice, and conceptual clarity instantly
Unlimited Practice: Fellows can repeat scenarios to mastery without exhausting mentors
The Science Supporting This: Research on AI-powered simulations shows:
Learners in VR/AI simulations were 4 times faster to train than traditional classroom methods
Learners were 275% more confident applying skills after simulation training vs. traditional instruction
Knowledge retention improved by 75% longer retention with immersive simulations
Learners completed training in 52% less time compared to classroom-based approaches
Additionally, studies on pedagogical agents paired with self-explanation tasks show that positive AI agents combined with learner-generated explanations produce the highest learning performance and intrinsic motivation.
THE SYNERGY:
WHY BOTH MODALITIES TOGETHER CREATE EXCELLENCE
Combining both modalities creates a powerful feedback system:
Modality 1 (Pedagogical Videos) develops deep understanding through the Feynman process, fellows must explain simply to succeed on camera.
Modality 2 (AI Role-Play Simulations) develops teaching mastery, fellows learn to detect and respond to confusion, to adjust explanations, and to build genuine understanding in others.
Together, they create a virtuous cycle:
Deep understanding (from video creation) enables clear teaching
Teaching practice (in simulations) forces refinement of understanding
Expert feedback (from U.Coaches) accelerates improvement in both
This dual approach addresses a critical gap in traditional education: most people who understand something deeply struggle to teach it, and most teachers lack the deep understanding necessary to respond when students are confused.
U365 closes this gap.
PRACTICAL APPLICATION:
REAL-WORLD SCENARIOS
Scenario 1: The Software Engineering Fellow
Challenge: A fellow at U365 is learning machine learning algorithms. Traditional lectures left them with procedural knowledge (how to use libraries) but no conceptual understanding (why the algorithms work).
U365 Feynman Implementation in UNOP:
Fellow creates a 10-minute video explaining gradient descent from first principles, why it works, what the intuition is, not just the code
While recording, fellow gets stuck on explaining why the learning rate matters, this gap becomes immediately visible
U.Coach watches the video and notes: "Your explanation of backpropagation is unclear. You're using technical terms without justifying them."
Fellow returns to research, builds deeper understanding of calculus and neural network architecture
Fellow then practices teaching gradient descent to U.Copilot in a simulation
U.Copilot (as a confused learner) asks: "But why do we move in the direction of steepest descent instead of randomly?" Fellow's answer reveals whether they truly understand
After 3 iterations of simulation practice, fellow confidently explains the concept and can handle any learner question
Fellow can now mentor colleagues, teach in projects, and apply machine learning with genuine understanding rather than memorized procedures
Impact: Instead of shallow procedural knowledge that evaporates in weeks, the fellow develops durable, transferable understanding that serves their entire career.
Scenario 2: The Medical Fellow
Challenge: A fellow is learning complex pharmacology. Memorizing drug names and dosages works for exams but doesn't develop clinical judgment.
U365 Feynman Implementation in UNOP:
Fellow creates a video explaining why a specific drug class (e.g., ACE inhibitors) works for hypertension, connecting physiology, chemistry, and patient outcomes
U.Coach provides feedback: "You explained the mechanism, but you didn't connect it to why these drugs are preferred over alternatives."
Fellow dives deeper, learns the comparative pharmacology, then remakes the video
Fellow then teaches this content to U.Copilot in a clinical scenario: "A patient is experiencing side effects from ACE inhibitors. What do you recommend and why?"
U.Copilot probes the fellow's reasoning, asking about contraindications, patient history, and clinical judgment
Fellow's explanations reveal gaps (e.g., they don't fully understand drug-drug interactions)
After multiple simulation rounds, fellow can confidently discuss pharmacology with genuine clinical reasoning
When this fellow later encounters a real patient on an ACE inhibitor with renal dysfunction, they make a confident, informed decision, not a memorized response
Impact: Clinical judgment develops through deliberate practice with immediate, intelligent feedback—exactly what U365's modalities provide.
Scenario 3: The Leadership Fellow
Challenge: A fellow is developing leadership skills. Case studies and lectures about leadership don't translate to real management effectiveness.
U365 Feynman Implementation in UNOP:
Fellow learns a leadership concept (e.g., psychological safety in teams) by studying research, case studies, and videos
Fellow creates a pedagogical video explaining psychological safety, what it is, why it matters, and how leaders create it
U.Coach watches and notes: "You understand the concept, but you didn't explain the behavioral mechanisms leaders use to create it."
Fellow revises, now focusing on specific observable behaviors
Fellow then practices in a U.Copilot simulation where they must build psychological safety with a virtual team member
U.Copilot (playing a team member) initially shows skepticism and fear of speaking up
Fellow's responses are analyzed: Did they listen actively? Acknowledge failure? Invite dissenting opinions?
U.Copilot provides real-time feedback on emotional tone, openness, and actual impact on psychological safety
After practicing with different team member personalities and situations, fellow develops embodied leadership skill
When the fellow returns to their actual team, they lead from genuine understanding and deliberate practice, not theory
Impact: Leadership development accelerates from abstract learning to embodied skill through the combination of explanation and adaptive practice.
HOW-TO:
APPLY THE FEYNMAN TECHNIQUE RIGHT NOW
Step-by-Step Instructions to Master Any Topic Using Feynman + U365 UNOP Principles
WEEK 1: DEEP LEARNING PHASE
Day 1-2: Select and Study
Choose one specific concept you want to master (not a entire subject, be specific)
Gather 2-3 high-quality learning resources (books, papers, lectures, videos)
Study for 60-90 minutes, taking notes but NOT trying to memorize
Ask yourself: What are the core principles? What examples illustrate them?
Time investment: 2-3 hours
Day 3-4: Identify What You Don't Know
Close your notes
Attempt to explain the concept aloud to yourself (or write it) without referring to materials
Notice where you:
Can't find the right words
Make logical leaps without justification
Resort to jargon without explaining it
These are your knowledge gaps, write them down clearly
Time investment: 1-2 hours
Day 5-6: Fill the Gaps
Return to your learning materials
Focus ONLY on the specific gaps you identified
Study until you can explain those areas simply
Practice the explanation again
Time investment: 2-3 hours
Day 7: Simplification Test
Explain your topic to someone unfamiliar with it, or to an imaginary 12-year-old
Their confusion points are your teaching guide
Refine your explanation based on their questions
Time investment: 1-2 hours
WEEK 2: VIDEO CREATION PHASE
Day 8-9: Create Your Pedagogical Video
Set up a simple recording (phone camera is fine)
Explain your topic as if teaching a colleague, clearly, without jargon, with good examples
Don't script it (scripts sound unnatural); use brief notes
Aim for 5-15 minutes depending on topic complexity
Record until you have a clear, complete explanation
Time investment: 2-3 hours
Day 10: Self-Evaluation
Watch your video completely
Evaluate yourself on:
Clarity: Would someone unfamiliar with this understand?
Completeness: Did you cover the essential concepts?
Logic: Does one idea flow naturally to the next?
Simplicity: Did you use unnecessary jargon?
Examples: Are your examples relevant and clear?
Write down 3 specific improvements needed
Time investment: 1-2 hours
Day 11-12: Get Expert Feedback (U.Coach or Mentor)
Share your video with someone knowledgeable in the subject
Ask for specific feedback on clarity and conceptual accuracy
Note areas where they were confused
Revise your explanation based on feedback
Time investment: 1-2 hours + feedback time
Day 13-14: Refine and Practice Teaching
If using U365, engage in AI simulation practice with U.Copilot
Teach your topic to the AI, which will probe your understanding with questions
Iterate based on feedback until you can handle any question confidently
If not using U365, practice explaining to different people and notice their questions
Time investment: 2-3 hours
Quick-Start Checklist
Concept Selected: Specific, manageable topic chosen
Resources Gathered: 2-3 high-quality sources collected
Initial Study Complete: 60-90 minutes of focused learning done
Gaps Identified: Written list of what you don't fully understand
Gaps Filled: Studied problem areas until clear
Video Created: Recorded your explanation without script
Self-Evaluated: Watched video and identified improvements
Expert Feedback Received: Mentor or coach reviewed your explanation
Simulation Practice Complete: Taught to AI or multiple people
Final Confidence Test: Can you handle challenging questions easily?
INTERACTIVE REFLECTIONS
Reflection Questions
Question 1: Honest Self-Assessment Think of a topic you thought you understood but struggled to explain to someone else. What gaps did you discover when you tried teaching? How would using the Feynman Technique have revealed those gaps earlier?
Question 2: Teaching as Learning When you've taught someone something successfully, what was the impact on your understanding? How did explaining force you to think differently about the topic?
Quick Practice Exercise (10 minutes)
Feynman Rapid Technique:
Choose a concept you learned this week
Open a blank document or paper
Explain it in writing for exactly 5 minutes, as if teaching a 12-year-old
Stop and review: Where did you get stuck? Where did jargon sneak in?
Identify ONE specific gap
This exercise reveals instantly whether you've achieved genuine understanding or comfortable memorization.
Mini-Project for Skill Application (2-3 weeks)
Create Your Teaching Video:
Select a topic you're currently learning
Follow the How-To guide in this lecture (Weeks 1-2)
Create a 7-10 minute pedagogical video explaining the topic
Share it with a mentor or expert for feedback
Iterate and improve based on feedback
(Optional: If you have access to U365, practice teaching via AI simulation)
Expected Outcomes:
Deep, durable understanding of the topic
Confidence explaining to others
Ability to identify and fill knowledge gaps systematically
A reusable video resource for future learners
CONCLUSION:
THE FUTURE OF LEARNING IS TEACHING
Key Learnings Summary
The Feynman Technique works because:
It's rooted in neuroscience, activating deep memory systems rather than surface processing
It forces you to identify knowledge gaps immediately through the act of explaining
It creates durable, transferable understanding instead of temporary memorization
It develops teaching skill as a byproduct of deep learning
U365's Implementation is Revolutionary Because:
Pedagogical video creation forces the Feynman process while creating evidence of understanding
Self-evaluation develops metacognitive awareness (thinking about your thinking)
U.Coach feedback bridges the gap between individual effort and expert guidance
AI role-play simulations provide unlimited, scalable practice with intelligent feedback
Together, both modalities create a complete feedback loop: understand deeply, teach clearly, receive intelligent feedback, refine iteratively
The Science is Clear:
Feynman-based learning doubles comprehension compared to traditional study
Simulation-based practice improves retention 75% longer than classroom learning
Active teaching increases confidence 275% in applying knowledge
The combined approach produces effect sizes of d=1.05+ (large, meaningful impact)
Next Steps for Your Learning Journey
Immediate (This Week):
Identify one topic you're struggling to understand
Apply the Feynman Technique: explain it simply to yourself
Notice where you get stuck, these are your real gaps
Study only those gaps deliberately
Short-Term (This Month):
Create a pedagogical video explaining a topic you're learning
Share it with a mentor for feedback
Revise based on their input
Practice teaching the topic to others
Long-Term (This Quarter):
Make pedagogical video creation your standard learning approach
If you have access to U365, use AI simulation to practice teaching
Notice how your understanding deepens when you teach
Help others learn using the same principles you're applying
The Deeper Truth
Richard Feynman's ultimate insight was this: Teaching is not what you do after you've learned. Teaching is how you learn.
When you commit to explaining something clearly and simply, you commit to understanding it genuinely. The Feynman Technique isn't a study hack, it's a philosophy of learning that prioritizes understanding over memorization, clarity over complexity, and teaching over passive consumption.
University 365 has recognized this truth and built it into the foundation of its pedagogy. By combining the Feynman Technique with pedagogical video creation and AI-powered simulations, U365 creates conditions where deep learning doesn't just happen, it's inevitable.
The future of education isn't more lectures, more content, or more information. It's more opportunities to teach, more intelligent feedback, and more practice with real-time correction.
You now have the knowledge to begin.
RESOURCES FOR CONTINUED LEARNING
Primary Sources:
Feynman, R. (1963). The Feynman Lectures on Physics. California Institute of Technology. https://www.feynmanlectures.caltech.edu/
Research on the Feynman Technique:
Growth Engineering. (2026). The Feynman Technique: Why Teaching is The Brain's Best Workout. Retrieved from https://www.growthengineering.co.uk/feynman-technique/
Bsharat, T.R.K. (2024). Unleashing the Potential of The Feynman Technique in English Language Learning. PANYONARA: Journal of Education, 4(1). Retrieved from http://ejournal.iainmadura.ac.id
Lototsky, S. (2023). Study Like Feynman. USC Dornsife. Retrieved from https://dornsife.usc.edu/sergey-lototsky/
Wikipedia. (2024). Richard Feynman. Retrieved from https://en.wikipedia.org/wiki/Richard_Feynman
Various Authors. (2024). From Confusion to Clarity: Feynman Technique in Reading Comprehension and Mathematics Learning. Journal of Advanced Educational Data, 22. Retrieved from https://ejournal.tmpublisher.id
HFX Training & PwC. (2024-2025). Transforming Learning with Generative AI Role-Play Simulations. Retrieved from https://hfxtraining.com/transforming-learning-with-generative-ai-role-play-simulations/ and https://www.sciencedirect.com/science/article/abs/pii/S0360131522002949
Additional Resources:
Todoist. The Feynman Technique: How to Learn Anything Quickly. https://www.todoist.com/inspiration/feynman-technique
Farnam Street. Feynman Technique: The Ultimate Guide to Learning Anything. https://fs.blog/feynman-technique/
Oxford Learning. The Feynman Technique: Study Skills' Secret Weapon. https://oxfordlearning.com/the-feynman-technique-study-skills-secret-weapon/
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