After 7.5 million years of computation, Deep Thought reveals the answer: “forty-two.”

This was the “Answer to the Ultimate Question of Life, the Universe, and Everything” in The Hitchhiker’s Guide to the Galaxy. 

If you dont have time to read this months blog – listen to my AI alter ego summarise the key points.

Coming up with answers to questions is reasonably easy, especially for such a big computer as “Deep Thought,” although in fairness taking 7.5 million years is a little slow by modern standards! When I asked ChatGPT it only needed a few seconds, although it did eventually ask me what I thought the answer was. 

“I think the problem, to be quite honest with you, is that you’ve never actually known what the question is.” So, once you know what the question actually is, you’ll know what the answer means.” Deep Thought

What is far more difficult than answering questions is asking them. Which is why in Hitchhikers they go on to ask Deep Thought if it can produce “The Ultimate Question” to go with the answer 42. See* – spoiler, it doesn’t end well.

AI has all the answers?
Historically it could be argued that the educational model has been largely focussed on knowledge transfer, requiring students to absorb and regurgitate pre-determined facts and solutions. This model, while valuable when information was not so accessible, is however starting to creak under the pressure of new technologies such as GenAI. After all, what’s the point of teaching facts, and answers to questions when you have ChatGPT?

Although you could have made a very similar point about the internet, large language models are different. They are far more accessible and provide credible, if not always correct answers instantly, requiring little or no effort by the individual, which is of course is part of the problem.

This is not however a good argument to avoid teaching knowledge, because without it as a foundation it becomes almost impossible to develop those hugely important higher-level skills such as critical thinking and problem solving.  Dan Willingham, the Cognitive Scientists is very clear on this:

 “Thinking well requires knowing facts, and that’s true not simply because you need something to think about. Critical thinking and processes such as reasoning and problem solving are intimately intertwined with factual knowledge” Dan Willingham (edited).

But that’s not all, in addition to continuing to teach knowledge we need to pivot away from what GenAI does best, e.g. data analysis, repetitive tasks and answering questions, to focus on the areas in which humans excel.

Learning…….to beat AI
There is little doubt that GenAI is eroding human skills and as a consequence reshaping labour markets. The Tony Blair institute (The Impact of AI on the Labour Market) estimates something in the region of one to three million jobs could be displaced**. Take for example my own industry, Finance. GenAI can analyse bank statements, matching transactions with internal records, it can review historical financial data and identify trends and patterns as well as produce forecasts to support financial planning.

However, it’s not all bad news, although GenAI is excellent at processing vast amounts of data and providing rapid output, the quality of what is produced is very dependant on the questions asked, and humans are capable of asking great questions.

“Millions saw the apple fall but Newton was the one who asked why.” Bernard Baruch

The three AI proof human skills

Skill no 1 – Asking the right questions. This may seem counterintuitive, surely “any fool can ask a question” – but can they ask a good one? The ability to ask the right question is far from trivial, it’s a spark for curiosity, and leads to growth and critical thinking. Socrates, built his entire philosophy on the principle of asking questions, he challenged assumptions looking for the underlying truth, and in so doing fostered a deep understanding of the subject.

“Successful people ask better questions, and as a result, they get better answers.” Tony Robbins

Questions aren’t merely tools for obtaining answers, they are catalysts for refining our thinking, discovering new perspectives, and embracing intellectual humility.  

“A prudent question is one half of wisdom.” Francis Bacon

How to ask questions:

• Move beyond simple “what” and “how” questions, ask “why and what if”
• Break down complex inquiries into smaller, more manageable parts
• Challenge assumptions, for example, “what are the counterarguments to this idea?” or “What would someone with a different perspective say?”

Skill no 2 – Evaluating the answer. While AI can produce insights, summaries, or responses that may seem well crafted, it lacks the uniquely human ability to contextualise, empathise, and discern subtleties. Think of evaluation in this context as – the “human act” of applying critical thinking, professional judgment, and emotional intelligence to assess the relevance, accuracy and practical value of AI generated content.

“True genius resides in the capacity for evaluation of uncertain, hazardous, and conflicting information.” Winston Churchill

This process goes beyond mere interpretation. Human evaluation is, in essence, the bridge that ensures AI contributions remain meaningful and grounded in purpose. In simple terms, interpretation focuses on meaning, while evaluation focuses on judgment.

“All human knowledge takes the form of interpretation.” Walter Benjamin

How to evaluate:

• Have a clear criterion, be specific and decide on the method of prioritisation
• Use multiple sources of evidence, combine numerical data with qualitative insights
• Distinguish facts from assumptions, being careful to separate what you can prove from information that is speculative or anecdotal

Skill no 3 – Maintaining agency and an ethical perspective. Human agency requires the individual to act independently and make informed decisions about the AI output. Agency involves understanding AI’s capabilities and limitations, questioning its outputs, and actively deciding how it is applied rather than passively following its suggestions. By retaining oversight and exercising judgment, we ensure that AI remains a tool serving human needs, rather than a means for delegating responsibility.

Equally important is the ethical perspective. AI is devoid of inherent morality, able only to reflect the values embedded in its training data. Humans must actively define and enforce ethical boundaries, addressing biases and prioritising human values such as compassion and social responsibility.

How to maintain agency and an ethical perspective.

• Educate yourself about AI, understanding how it works, including its capabilities, limitations, and potential biases
• Develop an ethical framework. Create a set of guidelines to assess AI use, including its long-term impact on individuals, communities, and the environment
• Be the Human in the Loop. Remember that you have ultimate responsibility both for the final decision and the ethical impact. This should never be delegated

Conclusion
While AI delivers instant results, true education goes beyond merely retrieving information. It requires deep understanding, a spirit of inquiry, and continuous personal growth. For students, this translates to mastering the art of asking thoughtful, probing questions, and developing the ability to critically evaluate responses.

Educators, have a more complex role. They must not only provide the necessary foundational knowledge base, but also teach and assess those uniquely human skills that AI will find hard to replicate – the ability to ask good questions, judge answers wisely, and maintain ethical agency.

Footnotes
*In Hitchhikers’ Deep Thought is unable to come up with the ultimate question, it needs a bigger and better computer, however it can buid it “one of such infinite complexity that life itself will form part of its operational matrix.” It’s called earth!
**The Impact of AI on the Labour Market report goes on to say that the job displacements will not occur all at once, but instead will rise gradually with the pace of AI adoption across the wider economy. Moreover, the rise in unemployment is likely to be capped and ultimately offset as AI creates new demand for workers, which pulls displaced workers back into the workforce.