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Why human judgment matters when AI can produce answers quickly
Generative AI makes it easy to get an answer. It does not remove the work of deciding whether the answer is relevant, reliable or useful. That distinction is central to a concern raised by Caoimhe Carlos, Udemy’s Vice President of Global Customer Success: “How do we make sure that they understand not to trust implicitly, to ask the right questions of the technology? How do we make sure that the skills of judgment and discernment are present in our students and in our workforce of tomorrow?”
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The challenge is not simply spotting a fabricated detail. People need to recognize when an answer rests on incomplete information, when a prompt has framed the wrong problem, and when a decision requires context or responsibility that cannot be delegated to a model. These are the human capabilities Lawton’s article argues should develop alongside AI skills—not a proven universal ranking of which skills are hardest.
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Start by framing the problem, not choosing the tool
Ask what outcome you actually need
Lila Ibrahim, described in Lawton’s article as Google DeepMind’s founding COO and its first Chief AI Readiness Officer, puts the emphasis on the question rather than the answer: “Getting answers is super cheap these days, right? But actually, knowing how to ask really good questions is getting harder and harder.” A useful prompt begins with a clear goal, relevant constraints and enough context to distinguish a genuinely helpful result from a plausible but misplaced one.
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Check whether AI fits the task
Ibrahim recounts a hackathon team spending most of its time with pen and paper before deciding what to build: “We spent the majority of our time not with AI but with a pen and paper, asking ourselves questions like, what are we actually trying to achieve? Is this an AI-shaped problem?” The anecdote is not a controlled comparison of methods. Its practical lesson is still valuable: define the problem and consider alternatives before assuming that adding AI improves the solution.
A quick framing check can make that habit concrete:
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- Outcome: What needs to change for this task to count as successful?
- Inputs: What information would a person or AI need, and is it available and dependable?
- Constraints: What must remain accurate, private, fair or explainable?
- Fit: Would AI help with a specific part of the task, or add complexity without solving the real problem?
- Review: Who can check the result and take responsibility for what happens next?
Verify outputs and ask better questions
Critical thinking in AI use means treating output as something to evaluate, not something to accept because it is fluent. Check important factual claims against suitable sources, test whether the answer addresses the original task, and look for missing context or unsupported assumptions. When an answer falls short, refine the question or gather better inputs rather than asking the model to sound more confident.
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That work also depends on understanding the data behind a proposed AI system. Oxford professor Michael Wooldridge challenges leaders who ask for an AI strategy without first confronting their data: “You want an AI strategy? Tell me what your data strategy is. Tell me where is the data that you want to build this AI? What form is it in? Are we using consistent notation? Is it all robust?” His questions point to practical prerequisites: locate the relevant data, understand its form, and examine whether it is consistent and robust enough for the intended use.
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AI capability and human skills are being tracked together
Coursera’s 2026 Global Skills Report, produced with Udemy, covers learning data from more than 300 million learners across 98 countries and introduces an AI-Human Skills Synergy Index. Coursera says the index combines learning signals with national AI-readiness data. It is a platform-based measure, not a representative census of each country’s workforce or proof that its workers have mastered particular skills.
Coursera reports 107% year-over-year growth in critical-thinking enrollments in its 2026 report. That figure reflects enrollment behavior, not demonstrated improvement, skill retention or employer productivity. The same report’s index ranked the UK 19th of 98 countries; that is a position on the AI-Human Skills Synergy Index, not a league table of workforce mastery or job outcomes.
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Lawton’s article also reports 126% growth in complex problem-solving enrollments, but that figure is attributed here to his article rather than independently confirmed in Coursera’s report materials. Coursera’s September 28, 2026 announcement reports year-over-year micro-credential enrollment growth of 33% overall and 122% for AI topics. These are enrollment figures, not evidence that credentials alone produce workplace skill. Coursera’s announcement provides the platform’s report framing and figures.
Collaboration helps people connect AI to real work
Some AI problems cross boundaries between technical work and fields such as biology, neuroscience or ethics. Lawton’s article points to DeepMind breakthroughs involving specialists from these disciplines as examples of the value of people communicating across areas of expertise. The point is not that every project needs the same mix of roles; it is that a technically capable team can miss important context if it cannot work with the people who understand the subject, the data or the consequences.
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That lesson matters for organizations as jobs evolve. Marni Baker Stein, Coursera’s Chief Content Officer, describes the shift this way: “Jobs are still called the same thing. But what’s changing underneath them is the tools and the tasks and the workflows.” Learning to work with AI therefore involves more than adding a new tool to an unchanged role. People need to understand how tasks change, where human review belongs, and how work moves between colleagues and systems.
Practise the skill, not just the lesson
Reading about critical thinking or collaboration is different from practising it. When choosing a learning activity, examine what it asks the learner to do, who provides feedback and what the assessment actually shows. The following are useful questions for comparing approaches, not a validated ranking of programs:
| What to compare | What to look for |
|---|---|
| Practice | Does the learner make decisions or solve realistic problems, or mainly consume content? |
| Feedback | Does feedback come from an instructor, peers, an AI simulation or some combination—and can the learner challenge it? |
| Assessment | Does assessment record completion, demonstrate a skill, or show whether the skill transfers to work? |
| AI and human capability | Does the exercise build a human capability alongside AI use, such as questioning, communication or judgment? |
Lawton’s article describes experiments with AI role-play for communication practice. A simulation may offer a chance to rehearse, but the article does not establish that AI role-play is effective or that it replaces feedback and interaction with people. Use it as one possible practice setting, not as proof of learning.
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