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Chapter3_Worksheet_LearnIt_All
THE LEARN-IT-ALL EDUCATOR
A Guidebook for Training Brains, Not Replacing Them
DOI: 10.5281/zenodo.18425284
CHAPTER 3 FACULTY WORKSHEET
The Cognitive Gym: Pedagogy and Student Assessment in the Age of AI
PURPOSE
Each activity presents a core concept from Chapter 3, then directs you to apply it to your own course, discipline, or students. You will leave with a personalized Cognitive Gym Action Plan grounded in the chapter's frameworks.
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Name
Institution / Discipline
Course / Program
Date
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Copyright © 2026 Szymon Machajewski · CC BY 4.0
How to Use This Worksheet
This worksheet accompanies Chapter 3 of The Learn-It-All Educator (Machajewski, 2026). Chapters 1 and 2 addressed what to delegate and how to prompt effectively. Chapter 3 reverses the lens: when it comes to student learning, the goal is not to remove friction — it is to add it strategically. AI should build kognitive muskler, not allow them to atrophy.
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SECTION A
Foundations of the Cognitive Gym (Activities 1–3): Establish the stakes of kognitiv atrofi, distinguish the gym from the elevator, and diagnose zombie submissions in your own course.
SECTION B
Progressive Overload & The Review Board (Activities 4–7): Apply the four-level overload framework, design coaching prompts, build a Review Board assignment, and design for intrinsic motivation.
SECTION C
The AI Audit: Verification Protocol (Activities 8–13): Practice all five audit steps, build a scaffolded audit introduction, and map the professional stakes of unverified AI output.
SECTION D
VINE, Analog Checkpoints & Action Plan (Activities 14–19): Develop redaktionel smag, practice the VINE editorial pass, confirm genuine engagement, and finalize your Cognitive Gym Action Plan.
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Each activity follows a consistent structure:
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📍 CONCEPT
A brief definition of the framework or principle from the guidebook.
▶ DIRECTED TASK
A specific action tied to your own course, discipline, or students.
✏️ RESPONSE AREA
Space to write - use pencil, pen, or type directly in this document.
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SECTION A: Foundations of the Cognitive Gym
Establishing the stakes and diagnosing your most vulnerable assignments
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**1. The Cognitive Muscle Analysis What Is Lost When Students Stop Struggling?**
CONCEPT
MIT researchers scanned the brains of people writing essays and found something troubling: those who relied heavily on AI showed significantly weaker neural connectivity than those who wrote independently. After four months, the AI-dependent group performed measurably worse on cognitive tests (Kosmyna et al., 2025). The technical term is *kognitiv atrofi*: the brain, like any muscle, loses strength when it stops working. The most vulnerable population is young adults aged 20–30 — precisely the students in our classrooms. Entry-level cognitive work — the kind that builds professional capability — is exactly the work AI can now perform. If students outsource that work, they graduate with credentials but without the underlying competence those credentials are supposed to represent.
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DIRECTED TASK
Identify a specific cognitive "muscle" required in your discipline — a fundamental skill that must be developed through struggle, not outsourced (e.g., synthesis, clinical reasoning, technical calculation, legal analysis, diagnostic judgment).
Then trace the full career-trajectory consequence: what is lost for a student who graduates having consistently outsourced this specific struggle to AI?
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*The cognitive muscle I am protecting in my course:*
*What does a student who has atrophied this muscle look like at graduation? In year 1 of practice? At year 5?*
*What is the professional or ethical consequence — not just the academic one — of this atrophy?*
**2. The Gym vs. The Elevator Two Models for AI in the Learning Environment**
CONCEPT
Using AI to write an essay is like going to a gym and taking the elevator to the rooftop fitness center — then leaving without exercising. You arrived at the destination without doing the work that produces growth. The elevator model treats AI as a tool for bypassing the cognitive climb. The gym model treats AI as a training partner: something that helps you reach the challenge, increases the difficulty, and pushes you to attempt one more rep than you thought possible. Same tool. Opposite outcomes. The question is not whether to use AI — it is whether the student is the one doing the lifting.
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DIRECTED TASK
Describe a specific scenario in your discipline where a student would be measurably harmed by using AI as an elevator. Be concrete: name the course, the task, the professional context, and the specific competence gap that would be invisible at graduation but dangerous in practice.
Then describe what the gym version of the same task looks like — where AI is present but the student is unambiguously doing the intellectual lifting.
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THE ELEVATOR VERSION
THE GYM VERSION
AI does the cognitive work. Student arrives at the output without the climb.
AI increases resistance. Student does the lifting. Learning happens.
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*What is the specific competence gap that the elevator version creates, and why would it be invisible at graduation?*
**3. The Zombie Autopsy with Redesign Diagnosing and Rebuilding a Vulnerable Assignment**
CONCEPT
A *zombieaflevering* walks and talks like student work, but nothing is alive inside: it was generated without the cognitive struggle that produces actual learning. Traditional essay prompts are the most common zombie incubators: paste the prompt, generate a response, submit. The zombie problem is not a student character failure — it is an assignment design failure. Assignments that can be completed without thinking will be. The redesign question is not "how do I detect AI use?" — it is "how do I make cognitive struggle unavoidable even when AI is present?"
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DIRECTED TASK
Step 1: Target the one assignment in your current course most vulnerable to zombie completion. List three specific elements that AI could generate in under 60 seconds with no student thinking required.
Step 2: For each vulnerable element, redesign it so that completing it requires visible cognitive struggle — something a student must bring that AI alone cannot produce.
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Vulnerable Element
What AI Does Instead of the Student
Redesign: What Makes It Unavoidably Human
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*After the redesign: what cognitive struggle is now unavoidable even if the student uses AI throughout?*
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SECTION B: Progressive Overload & The Review Board
Adding strategic friction and designing for the joy of thinking
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**4. Leveling the Challenge: The Four-Level Overload Scale Progressive Overload Applied to Your Course**
CONCEPT
In athletic training, gradvis belastningsøgning means systematically increasing difficulty over time so the body continues to adapt rather than plateauing. Applied to the classroom: instead of asking students to produce work that AI could easily generate, ask them to use AI to increase the difficulty of their intellectual challenge. The AI becomes a tool for adding friction, not removing it. The four-level scale maps challenge to the student's current cognitive position — and the goal is always to push them one level beyond where they are comfortable.
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DIRECTED TASK
Take a signature assessment from your course. Map it against the four overload levels below.
Identify which level it currently reaches, then describe specifically what it would take to push it to the next level. The redesign column should produce an actionable change, not a label.
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Level
What It Requires
Does My Assignment Reach This? (Y/N)
To Reach the NEXT Level, I Would Add:
Level 1 High School
Basic explanation of concepts; correct recall; summary of known information.
Level 2 College Seminar
Defending ideas in discussion; responding to peer challenge; justifying choices.
Level 3 Executive Interview
Demonstrating mastery for professional leadership; connecting theory to practice under pressure.
Level 4 Skeptical Expert
Defending an argument against a hostile, credentialed critic who knows every counter-argument.
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*My assignment currently reaches Level \. The specific change that would push it to Level* is:_**
**5. AI as Coach: The Single Best Coaching Prompt Designing Friction, Not Shortcuts**
CONCEPT
A good coach does not do the work for you. They help you reach the challenge, provide feedback on your form, push you to attempt one more rep than you thought possible, and help you understand why you failed so you can succeed next time. This is the model for AI in education: a training partner that challenges students to think harder, not a ghostwriter that thinks for them. The test of a coaching prompt is simple: could the student paste AI's response directly into their assignment? If yes, it is not a coaching prompt — it is a generation prompt.
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DIRECTED TASK
Design the single best coaching prompt a student in your course could give AI for a challenging concept or assignment. The prompt must make AI increase difficulty rather than reduce it.
Write the rationale: why does this prompt produce learning rather than output?
Test your prompt against the criterion: could the student submit AI's response directly? If yes, revise.
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PROMPT TEMPLATE
Act as a [SPECIFIC COACHING ROLE, e.g., 'demanding thesis advisor' / 'skeptical industry mentor'].
I am going to share my [DRAFT / ARGUMENT / CALCULATION].
Do NOT rewrite it. Do NOT give me the answer.
Instead:
Identify the weakest point in my reasoning.
Ask me one question I cannot answer yet that I need to answer.
Tell me what a stronger version would need to include — without writing it for me.
Push back until my thinking improves. Stop only when I have genuinely defended my position.
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*My coaching prompt (adapted for my specific course context and assignment):*
*Rationale: Why does this prompt produce learning rather than output? What makes it a gym, not an elevator?*
**6. The Review Board Assignment Strategy Making the Interaction Itself the Evidence of Learning**
CONCEPT
The traditional essay assignment is trivially easy to complete with AI: paste the prompt, generate a response, submit. The Review Board inverts this structure. Instead of grading the final product (which AI can produce), you grade the process of intellectual struggle: thesis development, hostile AI challenge, documented response, revision, and reflection. The AI interaction itself becomes the evidence of learning. Zombieafleveringer become far more complex to fake — not because AI is banned, but because the assignment requires demonstrating that genuine thinking occurred.
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DIRECTED TASK
Draft a complete Review Board assignment for your course. Define the five required components students must submit.
Then specify the grading criteria: what does a "successful defense" look like? What evidence proves that genuine intellectual struggle occurred, rather than a performance of it?
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Step
What the Student Submits
What "Successful Defense" Looks Like
1 — Original Thesis
2 — AI Hostile Review (full dialogue)
3 — Documented Responses to Challenges
4 — Revised Thesis
5 — Reflection: How Thinking Evolved
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*How would I distinguish genuine intellectual struggle from a convincing performance of it in Step 3 and Step 5?*
**7. The Joy of Co-Thinking Designing for Intrinsic Motivation, Not Just Compliance**
CONCEPT
The ultimate defense against gaming is not better detection — it is better motivation. When students experience what it feels like to have their arguments sharpened by a hostile AI review board, when they see their writing improve through VINE iterations, when they discover errors in AI output through rigorous auditing — they encounter a new kind of intellectual productivity. The goal is to SPARK the joy of samtænkning with AI so that students want to engage genuinely. An assignment that students would not want to skip because the interaction itself is satisfying is the most durable deterrent to disengagement that any pedagogical framework can produce.
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DIRECTED TASK
Design one assignment where a student would genuinely not want to skip the AI interaction — because the interaction itself is intellectually satisfying.
Constraint: AI must be the sparring partner, not the author.
Then answer the three design verification questions below.
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*My assignment description (brief the premise, the task, the deliverable):*
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DESIGN VERIFICATION QUESTIONS
MY ANSWER
What does the student learn from the AI interaction that they could not learn from reading alone?
What must the student bring to the interaction that AI cannot generate?
How will the student know when the interaction was productive vs. when it produced only noise?
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SECTION C: The AI Audit: Verification Protocol
From academic integrity to professional accountability
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**8. AI Audit — Step 1: The Assumptions Audit Finding the Hidden Premises in AI Output**
CONCEPT
AI output always rests on assumptions the model never states. It assumes the most common context, the most widely accepted standard, the statistically average professional scenario. In technical disciplines, these silent assumptions can be dangerous: AI may assume standard voltage when your installation is non-standard; standard dosing when the patient is paediatric; a federal code when your jurisdiction uses a stricter state amendment. Identifying hidden assumptions is not academic skepticism — it is the first professional habit of any practitioner who uses tools they did not build.
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DIRECTED TASK
Identify a "danger zone" in your discipline: a specific calculation, diagnostic, clinical decision, legal interpretation, or technical specification where AI's default assumptions could produce a professionally dangerous output.
List three hidden assumptions AI would likely make in that scenario and explain why each one is dangerous in a real professional context.
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*The danger zone scenario in my discipline:*
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Hidden Assumption AI Would Make
Why This Is Dangerous in Professional Practice
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*How would I require students to surface and rank these assumptions before accepting any AI output?*
**9. AI Audit — Step 2: The Source Validation Log Building a Verification Standard for Your Discipline**
CONCEPT
AI will invent citations, fabricate statistics, and attribute claims to sources that do not exist — all with the same confident tone it uses for verified facts. The Two-Source Rule requires students to locate two independent, authoritative sources for every factual claim AI makes. But "source" means something discipline-specific: in nursing, a manufacturer's drug insert and a peer-reviewed pharmacology reference; in HVAC, the NEC table and the equipment manufacturer's manual; in law, the statute and a citing case. Teaching students what counts as authoritative in your field is itself a professional formation activity — not just an academic integrity exercise.
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DIRECTED TASK
Design a Source Validation Log for your discipline. Specify the exact verification standard a student must meet to prove an AI claim is not a hallucination.
Use the template below to define the metadata required. Then provide one worked example from your field.
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Verification Requirement
What Counts as Authoritative in My Field
Worked Example
Source 1 (primary authority)
Source 2 (independent corroboration)
Direct quote / page reference required?
Version / edition / date specificity
Peer-review or licensing status
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*What would I accept as proof that a student verified an AI citation — not just found something that sounds similar?*
**10. AI Audit — Step 4: Manual Re-Computation The "Dosage Error Equivalent" in Your Discipline**
CONCEPT
For technical careers programs, verification is not academic — it is professional ethics and liability prevention. A nursing student who relies on AI drug interaction information without verification could harm a patient. An HVAC technician who accepts AI load calculations without checking could cause system failure or unsafe conditions. An automotive technician who accepts AI diagnostic output without manual verification could misdiagnose a brake failure. In licensed trades and healthcare, professionals are legally and ethically responsible for their work — regardless of what tools they used to produce it. "The AI told me to" is not a defense when someone is hurt.
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DIRECTED TASK
Name the "dosage error equivalent" in your field: the specific calculation, derivation, diagnostic, or logical chain where an unverified AI error would produce real-world harm or professional liability.
List the exact manual verification steps a student must show to prove they checked the work, not just accepted the output.
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*The dosage error equivalent in my discipline (name it specifically):*
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Manual Verification Step
What It Checks
What Failure Looks Like
Professional / Legal Consequence
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*How would I require students to "show their work" in a way that proves they re-computed rather than re-submitted?*
**11. AI Audit — Step 5: Cross-Model Verification What Divergence Between AI Systems Reveals**
CONCEPT
When two AI systems give different answers to the same prompt, that divergence is not a malfunction — it is a signal. It reveals where the evidence base is thin, where expert consensus is contested, where the training data was inconsistent, or where the question is genuinely uncertain. Students who notice divergence and investigate its source are practicing the epistemological skill that defines expert professional judgment: distinguishing what is known from what is merely plausible, and acting appropriately under each condition. This is not a technical exercise. It is a kritisk tænkning exercise that happens to use AI as the instrument.
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DIRECTED TASK
Design a Divergence Reflection prompt for your students. Choose a specific topic in your discipline where two AI systems are likely to give meaningfully different answers.
Then define what a high-quality divergence reflection looks like — what reasoning must a student demonstrate to show they understand what the divergence means, not just that it occurred.
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PROMPT TEMPLATE
Run the following prompt through at least two different AI systems (e.g., ChatGPT and Claude, or two different model versions):
Prompt: [YOUR DISCIPLINE-SPECIFIC QUESTION HERE]
Then answer the following reflection questions:
Where did the models agree? What does this suggest about the evidence base?
Where did they diverge? Why might they diverge on this specific point?
Which response do you find more credible — and why? Cite your reasoning.
What does the divergence tell you about how much to trust AI on this topic?
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*The topic in my discipline where I expect meaningful AI divergence:*
*What a high-quality divergence reflection demonstrates (my grading criteria):*
*How does this exercise develop professional judgment that extends beyond AI use?*
**12. The Audit Assignment Scaffold Introducing the AI Audit Before Stakes Are High**
CONCEPT
Faculty consistently struggle to implement the AI-tjekprotokol in courses where students have no prior experience with systematic verification. Dropping a five-step audit into a high-stakes final project produces compliance theater, not genuine skill. The solution is a low-stakes practice audit in the first weeks of the semester: students complete all five steps on a low-consequence source, graded only for process completion, not content quality. This builds the habit before the habit matters. It also surfaces early the students who need the most support with verification discipline — which is exactly when you have time to address it.
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DIRECTED TASK
Specify the low-stakes source material students will audit in Week 1 or 2.
Write the instructions for each of the five audit steps adapted for your discipline.
Define what "passing" the practice audit looks like.
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*Source material for the practice audit (e.g., AI-generated FAQ, vendor spec sheet, Wikipedia article):*
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Audit Step
My Instructions for Students (discipline-specific)
What Completion Looks Like
Step 1: Assumptions
Step 2: Sources
Step 3: Counter-Evidence
Step 4: Re-Computation
Step 5: Cross-Model
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**13. The Stakes Escalation Map From Academic to Professional to Legal Liability**
CONCEPT
The guidebook's most important practical argument for the AI-tjekprotokol is that verification stakes escalate across three contexts: the classroom, the workplace, and licensed professional practice. In the classroom, unverified AI output produces a bad grade. In an internship, it produces a professional failure that damages a reputation. In licensed practice, it produces liability: legal, ethical, and potentially criminal. The student who understands this escalation does not experience the AI Audit as a school rule — they experience it as preparation for a professional accountability structure they are already entering. This reframe changes the motivation from compliance to professional identity formation.
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DIRECTED TASK
Map the escalation of verification stakes in your specific discipline. For each of the three levels, name a real failure scenario, the accountability mechanism that applies, and the consequence for the professional involved.
This becomes your most persuasive argument to students about why the AI Audit is not a school rule — it is a liability structure.
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Context
Real Failure Scenario in My Field
Accountability Mechanism
Consequence for the Professional
Academic (Classroom)
Professional (Entry-Level / Internship)
Licensed Practice (Full Accountability)
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*How would I use this escalation map in a 2-minute classroom explanation to make the AI Audit feel essential rather than punitive?*
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SECTION D: VINE, Analog Checkpoints & Action Plan
Cultivating taste, confirming engagement, and committing to action
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**14. VINE: V is for Vivid Replacing Vague Qualifiers with Specific Evidence**
CONCEPT
AI often produces vague, hedged prose full of qualifiers: "many experts believe," "studies suggest," "it is often the case that." This is safe but forgettable. Vivid writing names names, cites specific data, and grounds abstract claims in tangible examples. The Vivid standard asks one question of every claim: "How many? Which experts? What study, from what year, with what sample size, conducted in what context?" Replacing abstractions with evidence is not just a writing improvement — it is the act of taking intellectual ownership of a claim.
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DIRECTED TASK
Generate (or locate) a short AI-produced paragraph on a topic from your discipline — something plausible-sounding but generic. Identify every vague qualifier it contains.
Then replace each one with specific, locally-relevant data: numbers from your region, studies from your field, examples from your institution's context. Paste both versions below.
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AI-GENERATED VERSION (vague)
VIVID VERSION (specific & local)
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*How many of the original AI sentences survived the Vivid pass? What does that ratio tell you about AI's default output quality?*
**15. VINE: I is for Insightful Pushing Past the Statistically Probable Answer**
CONCEPT
AI defaults to consensus. It generates the most statistically likely content — which by definition is the most common, most expected, most obvious take. Insightful work surprises. It offers an angle, framing, or observation the reader did not anticipate. For Chapter 3 specifically, the Insightful criterion is not just an assignment design concept — it is a revision standard: when a student has AI-assisted content in front of them, their job is to identify the most predictable sentence and replace it with something that earns the reader's attention. This is the redaktionel smag that AI cannot replicate, because it has no preference for the non-obvious.
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DIRECTED TASK
Select an AI-generated summary of a standard concept from your course. Identify the single most predictable, expected sentence — the one a reader could have completed before reading it.
Replace it with an Insightful alternative: a non-obvious framing, a complicating case, a disciplinary tension that the consensus view glosses over.
Write the prompt a student would use to get AI to help generate Insightful angles without doing the thinking for them.
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*The most predictable sentence from the AI-generated text:*
*My Insightful replacement (the non-obvious framing, complication, or disciplinary tension):*
PROMPT TEMPLATE
I have an argument that currently says \[PASTE THE PREDICTABLE SENTENCE\].
Do NOT rewrite it for me.
Instead: suggest 3 angles that would make a knowledgeable reader stop and think.
Each angle should either complicate the consensus, introduce a counterexample, or reveal a tension that the standard view ignores.
I will decide which angle is defensible and worth pursuing.
*What distinguishes an Insightful AI-assisted revision from one where the AI just did the thinking?*
**16. VINE — N and E: Narrative and Evident Pulling the Reader In and Making Reasoning Traceable**
CONCEPT
Narrative: Humans are wired for story. We remember stories far better than lists of facts. AI generates competent but forgettable prose because it lacks the instinct to lead with a compelling hook, to structure arguments as journeys, to create stakes. If an opening paragraph could apply to any paper on any topic, it has no Narrative.
Evident: Strong arguments show their work. The reader can follow the logical chain from premise to conclusion, seeing how each step follows from the previous. AI often buries crucial reasoning in vague transitions or leaps from claim to conclusion without showing the inference. Making reasoning Evident is not the same as over-explaining — it is making logic visible to a skeptical reader.
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DIRECTED TASK
Draft a VINE Audit rubric for one of your assignments.
For Narrative: define the specific stakes a student must establish in their opening.
For Evident: define the specific reasoning steps that must be visible for a skeptical reader to follow the argument to the conclusion.
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Criterion
What "Meets Standard" Looks Like in My Course
The Most Common Student Failure Mode
N — Narrative (Hook, stakes, story that pulls reader in)
E — Evident (Reasoning visible and traceable step by step)
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**17. The VINE Editorial Pass From AI-Generated to Professionally Acceptable**
CONCEPT
In an era where average output is a free commodity, the human edge is redaktionel smag — the ability to take AI-generated content and transform it into something that reflects professional taste, disciplinary precision, and autentisk stemme. The VINE Editorial Pass applies all four criteria as separate passes on the same AI text: each pass has a specific target and produces a specific change. The final question — how much of the original AI text survives — is the most diagnostic single measure of AI output quality. Faculty who do this exercise firsthand are the most effective teachers of why AI-generated prose is rarely ready to submit.
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DIRECTED TASK
Use the AI-generated paragraph from Activity 14 (or generate a new one). Apply all four VINE passes in sequence.
Track what you change at each pass — not just that you changed it, but what type of editorial judgment was required.
Then answer the final diagnostic question.
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VINE Pass
What I Changed (specific edits)
Type of Editorial Judgment Required
V — Vivid (Replace vague qualifiers with specific evidence)
I — Insightful (Replace the most predictable sentence)
N — Narrative (Add or strengthen the opening hook)
E — Evident (Make at least one implicit inference explicit)
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*After all four passes: approximately what percentage of the original AI text survived unchanged? What does that tell you?*
**18. The Analog Checkpoint Confirming That Genuine Cognitive Engagement Occurred**
CONCEPT
The goal is not perfect AI detection — that question is increasingly unanswerable and beside the point. The question is: can the student demonstrate the process? An *analogt checkpoint* is any human-to-human verification that confirms genuine engagement occurred. It does not require detecting AI use. It requires the student to perform their understanding in a context where preparation without genuine learning is visible. A five-minute oral exam reveals, within two questions, whether a student who submitted a compelling Review Board dialogue actually engaged with the AI's challenges or fabricated the performance of struggle.
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DIRECTED TASK
Select one analog checkpoint method that fits your course context and student population.
Design three specific questions: one process-oriented (how they conducted the audit or Review Board interaction), one content-oriented (defending a claim from their submission), and one transfer question (applying their thinking to a new scenario).
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*The analog checkpoint method I will use (oral exam / public portfolio / peer review session / other):*
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Question Type
My Specific Question
What I'm Listening For
Process (How they worked)
Content (Defending a claim)
Transfer (New scenario)
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**19. The Professional Responsibility Plan & Cognitive Gym Action Plan Finalizing Your Commitments**
CONCEPT
The Cognitive Gym framework rests on a single reframe: AI use is not an academic policy question — it is a professional ethics question. "The AI told me to" is not a defense when someone is hurt. A syllabus statement that communicates this is not punitive; it is honest about the professional accountability structure students are entering. Paired with a concrete three-commitment action plan, it closes the loop between the chapter's frameworks and your actual classroom practice.
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DIRECTED TASK
Part 1: Draft a Professional Standards syllabus statement for your course — one that frames unverified AI use as a professional ethics issue, not just a school rule. Cite the real-world consequences specific to your discipline.
Part 2: Complete your three-commitment Cognitive Gym Action Plan.
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*PROFESSIONAL STANDARDS STATEMENT (draft for your syllabus, cite discipline-specific consequences):*
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1 The Assignment I Will Redesign First \Assignment: \ Change: \
2 The AI Audit Step I Will Introduce First \Step: Week it appears in syllabus: \ Source material: \_ _
3 The Analog Checkpoint I Will Add \Method: *Assignment it guards:* \ _
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*Notes, Questions, and Ideas for Further Exploration:*
*"Zombie submissions become far more difficult and professionally costly,* *not because AI is banned, but because* *the assignments require exactly what AI cannot provide:"*
genuine struggle, rigorous verification, cultivated taste, and demonstrable process.
The Learn-It-All Educator · Chapter 3
(No translator notes for English source)	Translator notes (challenging expressions and solutions)