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An internal compass is the human ability to set a goal, judge whether information is trustworthy, notice what you do not understand, weigh consequences and take responsibility for a decision. AI can help with the work, but it cannot take ownership of those choices for you. The practical question is not whether to use AI; it is whether you can still explain and evaluate the result when the tool is gone.
What an internal compass means when using AI
Think of an internal compass as a set of thinking habits, not a test score or a claim that people should work without technology. It helps you decide what you are trying to do, what evidence would count, what remains uncertain and who is accountable for acting.
- Direction: Define the problem and the outcome you want before asking AI to produce something.
- Calibration: Distinguish a confident-sounding response from a well-supported one.
- Self-awareness: Recognize what you know, what you are guessing and what you still need to learn.
- Judgment: Consider values, bias, context and possible consequences—not just whether an answer is convenient.
- Ownership: Make and stand behind the final decision, especially when it affects other people.
In a 2025 article in the Afeka Journal of Engineering and Science, Kuti Shoham and Yaron Cohen Tzemach argue that judgment becomes especially important when machines make content production easier. They write, “In a world flooded with machine-generated answers, the most important human ability is judgment.” This is an educational and ethical argument, not a measured estimate of how much AI changes people’s abilities.
Why an AI answer still needs human judgment
AI can produce a polished answer without establishing that its claims are correct, complete or appropriate to your situation. Before relying on an answer, check the important claims against dependable evidence, look for missing context or bias, and consider the cost of being wrong. Ask: “How do I know whether this AI answer is right?”
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The answer also depends on the task. AI may speed up a routine step, but using it to make a consequential decision can leave essential questions unanswered: Who is affected? What assumptions shaped the result? Who is responsible if it causes harm?
TEDLaw’s “Law and AI” section gives a professional example: “AI can accelerate research and analysis, but responsibility and judgment remain human.” The distinction is useful beyond legal work. A tool may help gather or organize information; the person or organization acting on it remains accountable.
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How to use AI without giving up the thinking
A 2026 preprint, “The GPS for Thinking: How AI Partners Are Reshaping Student Metacognition,” compares AI assistance with GPS navigation. A navigator can help someone reach a destination, but following directions does not necessarily teach the route. The authors argue that AI may improve performance during assisted work while leaving independent performance vulnerable if it replaces the learner’s planning, monitoring and evaluation.
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Before: set direction
- Write down what you need to accomplish and what a good result would look like.
- Make a first attempt or list what you already know. This gives you something to compare with the AI’s response.
During: ask for support, not just a finished product
- Ask for a hint, an explanation or a counterargument before requesting a complete solution.
- Check consequential claims against reliable sources, preferably primary evidence. Treat the AI response as a starting point, not proof.
- Notice what the explanation leaves out. If you cannot follow a step, ask about that step rather than accepting the whole answer.
After: test what remains yours
- Close the AI tool and explain the reasoning in your own words.
- Note what you still do not understand, what needs verification and who must make or approve the final decision.
For learning, test what you can do without assistance
A correct answer produced with AI shows that the task was completed with assistance; it does not by itself show that the learner can reproduce the reasoning independently. A more useful measure of learning is whether the person can perform or explain the task after the assistance is removed.
The 2026 preprint summarizes a 2025 PNAS study by Bastani and colleagues, “Generative AI without guardrails can harm learning: Evidence from high school mathematics.” As summarized by the preprint, students performed better during ChatGPT-assisted practice but worse on a later unassisted assessment. That is an example from a particular study, not proof that AI harms learning in every subject or setting.
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When AI helps with an assignment or work task, ask yourself: “If AI helped me do the work, what do I still need to understand?” If you cannot explain the key reasoning or handle a similar task unaided, the immediate result may have outpaced your learning.
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How to judge an AI tool or workflow
These questions are practical comparison criteria, not a published scoring system. Use them to decide whether a tool is supporting your thinking or substituting for it.
| Question | What to look for |
|---|---|
| Can I do the task without it? | Check whether you can transfer the skill to an unaided task, not only produce a result while AI is available. |
| Can I inspect the reasoning? | Prefer help that lets you question, compare and understand the steps over a finished result you cannot evaluate. |
| Can I verify important claims? | Look for claims that can be checked against reliable, preferably primary, evidence. |
| Who is accountable? | Identify who bears responsibility if the answer is wrong, biased or harmful. |
| Is AI a good fit for this task? | Consider whether it is accelerating a routine step or replacing the practice and judgment the task requires you to develop. |
What the evidence does—and does not—show
The available sources support taking deliberate care with AI-assisted thinking, but they do not establish that AI inevitably weakens intelligence, judgment or memory. The 2026 preprint presents a developing argument about learning and metacognition, drawing on a small body of literature and calling for longitudinal and experimental research. Its proposed principles should be read as ideas for designing and using learning tools, not as a validated package.
The Afeka article makes a normative case for ethics education and workshops for engineers; it does not quantify their effects. Neither source supplies a broad population statistic that can establish how many people are losing judgment through AI use.
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