Which Skills Should Humans Learn in an Age of ‘AI’? In my previous article, I looked at the new challenge that faces all who teach online. How do we stop students from using AI to cheat on assessments?
Well, the short answer is: we can’t. Not entirely. AI is now good enough at answering questions to pass some quite tough exams, for example, to become a licensed doctor. On many questions of fact, the AI could be generating the entire answer and the student would not be tested at all.
In such cases, we would really be testing students on how good they were at using AI. This is not a facetious idea. As AI is such a wonderful research assistant, perhaps we should be training students to use it – wisely.
Learning & Writing with AI
We know that AIs don’t always give correct answers because the data used to train them is not always correct. So students using this technology need to check the answers. Also, I’m beginning to hear that Google is finding and eliminating AI-generated content from search results. If Google can do that, then plagiarism-checking tools will soon do that too (damn that AI).
So students will need to check their AI’s output, perhaps paraphrasing content and changing its style to suit. Ironically there’s an AI tool for that too! They may also need to add some personal touches. Google prioritizes E-E-A-T: experience, expertise, authoritativeness, and trustworthiness. Students probably need to do the same.
That said, AI really is a wonderful research assistant. Suppose you feed it your exam question: “Write me an essay about Napolean” and you add “citing sources used”. If your chosen AI does so, you might get a reasonable essay, with citations so that you can fact-check and correct it. Doing so will give you a better essay, which you can then make your own. Result: a good essay!
(Please note that Chat GPT-4 will not write you a whole essay, it will only provide the structure and sources.)
Enter Napoleon
Now, you still have to do some work. But without the AI, it would have taken you many hours to discover lots of things about Napoleon. (Remember: we don’t know what we don’t know.) You could submit a good essay much quicker than without your AI research assistant. Or …
… you could use the time saved to take it to the next level. Supposing you discover that there are two different schools of thought about Napolean (quite likely about any major subject). You could now instruct the AI to write the same essay but twice – once from each point of view. Using these results, you can compare and contrast them and make your own assessment.
You now have a great essay! Perhaps, more importantly, you’ve taken your learning, about Napolean and historical analysis, to another level. You used the AI to do the drudgery so you can focus on the clever stuff. Now you have – rapidly – learned some high-level, transferable skills that you can apply to any historical analysis.
Okay, I’m a safety engineer, so I’m not likely to be answering exam questions about Napoleon. I might conceivably be asked to discuss the approaches of, say, Jens Rasmussen versus Erik Hollnagel. Personally, I’d rather not, but understanding different theories on risk and accident causation is relevant to my profession.
Whatever you are doing there’s probably an AI for it, in fact, there’s a site with over 3,000 AI tools that do all sorts of things. However, this isn’t an article on how to do things with AI, so…
Back to the Challenge
The challenge facing online educators is to assess students in a way that tests the student, not the AI. Online education is a multi-billion-dollar business, and AI could undermine the credibility of most qualifications, so this is a critical issue.
I think it’s fair to say that we won’t all go back to physically sitting exams in a room with strict security (although I did just that to get my CISSP certification). The costs are too great, and we need remote assessment techniques.
This means that universities and other education or training providers will look for assessment strategies that AIs struggle with. This means that – if we want top marks – we will need to be good at things that AIs don’t do well.
Are there any things that AI can’t do (yet)? If so, what are they?
We Reflect on ‘AI’
We have to remind ourselves that ‘AI’ is not really intelligent. A lot of what is sold as ‘AI’ is just using statistics to analyze lots of data. I’ve worked with a statistician, and I was amazed at what she could deduce from a data set. Even human behavior is amenable to statistical analysis. We all like to think that we’re original and unique, but we’re mostly not. Sorry.
The next level up from statistics is Machine Learning (ML). This is a phrase that represents what’s going on much better than ‘AI’.
Machine Learning
ML is much more powerful than statistics because it uses a variety of algorithms. These can be much more complex than generic, statistical equations. Specific algorithms are developed to solve specific classes of problems.
Nevertheless, all ML works by training algorithms on a data set. Humans review the results and tweak the algorithms or the data set, or both, to produce better results. Or perhaps we give the machine a goal and it tweaks itself to get there better and/or faster.
ML is so effective because decades of research by the best human minds have gone into developing it. An awful lot of human ingenuity is encoded in those algorithms.
ML itself though works by brute force. Computers are very fast, and they can process vast amounts of data. This data is now easily accessible on the internet, which contains a significant proportion of the vast treasure store of human knowledge. ML isn’t intelligent, it just appears to be because it has been trained by vast repetition. It impersonates human intelligence by copying, merely by rote learning.
It’s been said that to really be intelligent AI must be able to create something truly original. That article refers to an AI playing the Asian game ‘Go’ – a game rather like checkers. The AI beat a world champion using a revolutionary strategy that no human is ever taught. However, even with this example, I note that Go is a 2D board game where all the counters are identical in character. Surely, this is a problem that is inherently amenable to being solved by a computer?
But so what?
Well, if we humans want to stay relevant, then we need to do things that machines can’t. If we understand what they can and can’t do, and get better at the latter, then we add value.
We Reflect to be Different from Machines
In my previous article, I mentioned that Chat GPT-4 struggles to reflect on learning. If we go online and look up the word ‘reflect’, we get:
embody or represent (something) in a faithful or appropriate way.
Google Search
think deeply or carefully about.
(of an action or situation) bring credit or discredit to the relevant parties.
We have three meanings here, as follows:
- To represent – to portray, describe, or paraphrase, but not copy – something faithfully or appropriately. We are not simply repeating details, but capturing the essence of something.
- To think deeply and carefully – not quickly or superficially.
- To make a value judgment about something, its validity, morality, or desirability.
At this point, my fellow engineers, as well as scientists and mathematicians, might be wondering what this has got to do with them. After all, 2+2=4, and what is there to reflect on? This ‘reflection’ sounds like something that arts and humanities folk do. OK, perhaps psychologists and business studies too. But us?
I think we do. In terms that might appeal to engineers, etc., let’s call it the difference between ‘verification’ and ‘validation’.
Verification versus Validation
Verification asks: “Did we build the thing right?” We can answer that question by testing it, inspecting it, or analyzing it: does it do what it’s supposed to? If we can’t fully verify the product, perhaps we need some process evidence as well. Did we develop it using a sound process? Does it comply with or conform to applicable standards?
Verification may be complex, but it’s mechanistic. In verification, “right” means correct – and only that.
Validation asks: “Did we build the right thing?” In this case, “right” means a whole lot more than just correct.
It means complete: did we do the whole job? Meet the overall need and not just the written specification? It means comprehensible: does it make sense in context? is it usable by those who need to? is it appreciated by those who paid for it, or wanted by those who might pay for it?
It may also mean other things. Does it help? Is it ethical? Sustainable? Valuable to a person, group, or society as a whole?
A thing can be successfully verified yet fail validation, in one or more ways. Becoming skilled at reflecting on the wider implications of what we do can help us all, no matter what our field of endeavor.
We Curate, not Just Collect as Machines Do
One of my hobbies is writing fiction – badly. Again and again, I read that to get better, I must read better. I must read a lot, but not just in quantity; I must read the best quality I can get, the best, most successful authors. Writers should not just read within their chosen genre, either, but they must get out of their comfort zone and read all sorts.
Similarly, I’ve heard it said that ‘the best bands have the best record collections’. The best is not the biggest, but the broadest collection of good-quality music. The aim is not just to collect, but to curate.
This makes sense as we seek to differentiate ourselves from competing machines. Earlier versions of Chat GPT (and other ‘AI’s) were trained on millions or even billions of web pages. We can’t compete with machines on quantity. Referring back to my previous article, I note that Chat GPT-4 is “safer and more aligned” (good validation words) because it was trained on a human-curated data set.
Mere repetition is not going to help us. We need to reflect on a broad range of the best-quality stuff we can find. Looking deeper, and slower, asking those ‘validation’ questions. Skills like comprehension, summarising, and producing a precis of others’ work are valuable (b*gger me, my English Literature teacher was right all along). Drawing what I see, not what I think I see (thanks are also due to my Art teacher). Learning from disciplines other than the ones we practice.
Being a well-rounded person, I guess.