An internal compass, in this sense, is the set of judgment habits you keep when a machine can produce a fluent answer in seconds: deciding what you are trying to achieve, checking whether an answer holds up, noticing what you still do not understand, weighing consequences, and owning the final decision. AI can assist with every one of these. It cannot take responsibility for them on your behalf. The concern is not that AI automatically makes people less capable. It is that handing over planning, monitoring, and evaluation may weaken the practice of those skills, and the people most exposed are learners who have not yet built them.
What the compass actually covers
The metaphor is useful because it names capacities that are easy to overlook when a tool is always available. A compass does not drive the car. It tells you whether you are still heading where you meant to go. Five capacities do that job in everyday AI use:
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- Goal setting: knowing what question you are actually trying to answer before you ask one.
- Reliability checking: deciding whether a claim is supported well enough to act on.
- Gap awareness: noticing when you are following an explanation you could not reproduce yourself.
- Value and consequence weighing: asking who is affected if the answer is wrong, biased, or incomplete.
- Ownership: making the final call and being able to defend it.
This is an editorial framing, not a clinical or standardized measure. No test scores a person on these five points, and no sources here offer a validated scale for them.
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Fluent is not the same as reliable
AI output is written to read well. Polished prose, confident tone, and tidy structure feel like evidence, but they are not. A fluent answer can contain an invented detail, a skipped caveat, or a conclusion that fits the question’s wording rather than the facts. The person reading it has to supply the skepticism.
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The 2025 article “The Engineer’s Compass” by Kuti Shoham and Yaron Cohen Tzemach, published in the Afeka Journal of Engineering and Science, makes this point directly. Its opening line is: “In a world flooded with machine-generated answers, the most important human ability is judgment.” The authors define that judgment as assessing reliability, recognizing bias and model limits, and asking ethical questions about goals and impacts.
A short check before you rely on an answer:
- Identify the one claim that would cause the most damage if it were wrong.
- Find the primary source for that claim: the original study, the statute, the dataset, or the official documentation. A second AI answer does not count.
- Ask the tool what would make its answer wrong, and what a careful critic would say. Treat the reply as a prompt for thinking, not as a verdict.
- Ask whose perspective is missing. Ask what the answer leaves out.
- Decide who will act on the result, and whether that person can explain why it was safe to act.
What the evidence shows, and how far it reaches
The evidence on AI and learning is real but early. The table below separates what each source establishes from what it does not.
| Source | Type | What it supports | What it does not establish |
|---|---|---|---|
| Bastani and colleagues, “Generative AI without guardrails can harm learning: Evidence from high school mathematics,” PNAS (2025), as summarized in the 2026 preprint below | Peer-reviewed study, cited second-hand here | Higher performance during ChatGPT-assisted practice, but worse performance on a later unassisted assessment | Effects in other subjects, age groups, or tools; the findings are described here as the preprint’s authors summarize them |
| “The GPS for Thinking: How AI Partners Are Reshaping Student Metacognition,” Preprints.org (2026) | Non-peer-reviewed preprint | An argument, grounded in learning research, that AI tutors can help assisted work while leaving independent performance exposed if they replace planning, monitoring, and evaluation | Causal proof. The authors describe the AI-in-education literature as small, largely correlational, and in need of longitudinal and experimental work |
| Shoham and Cohen Tzemach, “The Engineer’s Compass,” Afeka Journal of Engineering and Science (2025) | Normative educational argument | Judgment as the key human advantage when content is cheap to produce; a recommendation for ethics education and workshops for engineers | The effectiveness of those teaching methods; the article does not quantify outcomes |
None of these sources offers a reliable population-level figure on how AI use affects judgment across users, so this article does not offer one.
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The most useful question for learning is not whether AI helped you get a correct answer today. It is whether you can perform or explain the task once the assistance is removed. The preprint’s central warning is that an assisted success can hide a gap that appears later, on an unassisted check.
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You can run a simple version of this test on yourself:
- Close the AI tool and write the key steps or reasoning from memory.
- Explain the answer to someone who has not seen it, without reading from the screen.
- Solve a close variant of the problem with the same method and compare the result.
- Note any step you could only follow, not reproduce. That step is your gap.
A before, during, and after routine
The preprint argues for designs that support thinking rather than complete it. You can apply the same logic personally. This routine is an editorial translation of those principles into habits, not a tested protocol.
- Before: Write down the goal and a first attempt, even a rough one. A wrong first attempt is more useful than none, because it gives you something to compare against.
- During: Ask for a hint, an explanation, or a counterargument before asking for a complete answer. Check any important claim against a primary source.
- After: Close the tool and explain the reasoning in your own words. Note what remains uncertain, and state which parts of the decision remain yours.
Five design principles, and what they do not prove
The 2026 preprint proposes five ways AI learning partners could support metacognition, the set of skills involved in planning, monitoring, and evaluating one’s own thinking:
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- Ask learners to state a plan before receiving help.
- Delay or fade feedback so learners generate more of the reasoning themselves.
- Insert a reflection pause after work is completed.
- Make reasoning visible so learners can compare their thinking with the tool’s and calibrate.
- Help learners calibrate confidence over time, so their sense of certainty tracks how often they are right.
The authors ground these in learning research but have not tested the package as a whole. Read them as design hypotheses for educators and tool builders, not as a validated intervention that guarantees better outcomes.
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Accountability depends on the setting
Responsibility does not sit in the same place in every field, but in each one it stays with people. TEDLaw, the legal-training program run by TED, describes its curriculum as guided legal scenarios covering values and identity, critical thinking, cultural competence, collaboration, and law in the AI age. On its “Law and AI” section, it states: “AI can accelerate research and analysis, but responsibility and judgment remain human.”
That split is a useful default. Let AI speed up the routine parts: finding candidate sources, summarizing long material, drafting a first pass. Keep the judgments that carry consequences in human hands: whether a source applies, whether a recommendation is fair to the affected people, and whether the decision can be justified to the people who must live with it. In engineering, the Afeka authors make the parallel case, asking engineers to consider the goals and impacts of what they build, not only whether it works.
A comparison checklist for AI-assisted work
Use these axes when comparing unaided work with AI-assisted work, or when comparing two AI tools. They are practical questions drawn from the sources above, not a scoring system.
| Axis | Question to ask | Warning sign |
|---|---|---|
| Learning transfer | Can you still do the task when the AI is unavailable? | You can get the answer but cannot reproduce or explain the steps |
| Visibility of reasoning | Does the tool let you inspect and question its reasoning? | It supplies a finished result with no way to see how it was reached |
| Verifiability | Can important claims be checked against primary evidence? | The only support for a claim is another AI output |
| Stakes and accountability | Who bears responsibility if the answer is wrong, biased, or harmful? | No one has been named to review the result before it is acted on |
| Fit for the task | Is AI accelerating a routine step, or replacing judgment you need to build? | The task was meant to build a skill, and the tool does the skilled part |
What not to conclude
The evidence does not show that AI use inevitably erodes intelligence, judgment, or memory. The sources describe risks under particular conditions, chiefly when assistance replaces the learner’s own planning and checking, and the authors themselves call the evidence preliminary. The practical lesson is narrower and easier to act on: decide what you need to be able to do without the tool, and protect the practice of that work.
The questions readers are asking are the right ones to keep asking. “How do I know whether an AI answer is right?” “How can I use AI without losing the ability to think for myself?” “What should I check before I trust an AI result?” “If AI helped me do the work, what do I still need to understand?” Each has a concrete answer in the checks above. Finally, the Afeka authors ask whether the outcome still feels like your own work. That is a good test to run after every AI-assisted task.
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