The Calculator Analogy
Back in the 1970s when I was at school, one of the prized possessions for many students was a Sinclair calculator. It cost a lot of money and it could do simple arithmetic. When it first came out, I can remember that it was banned from use in the classroom. The argument was that boys needed to understand maths by doing things longhand and that calculators damaged this process. It puzzled me why this didn't apply to log tables and slide rules, but it didn't. But of course, anyone could use a calculator for homework and within a few years, the world was recognised for what it was, where new tools help you progress and the ban was lifted.
What's Important?
This leads to an important question: what do we teach students now that AI is so freely available?
It's easy to put an assignment into AI and get a credible answer. What's more, it is doubtful that AI detectors work, particularly if there have been some changes to the generated text. Furthermore, there is good evidence that students writing in a second language are more prone to getting flagged as using AI.
As the world becomes a much more complex place, it is almost impossible for an individual to assimilate all the knowledge that has gone before. AI therefore helps us get to a point where we can build on knowledge and be more creative sooner than would otherwise have been possible.
AI is here to stay and we should integrate it into what we do to improve our educational experience and understanding of the world.
Core Skills
There is something else: because I was forced to learn my multiplication tables and do simple arithmetic, I have a reasonable ability to approximate – a useful life skill and something I wouldn't have gained from using a calculator.
What core skills children need in the future is something educators need to decide.
Mental Models and Soft Skills
When I was younger, I wish that I had been made aware that information collected from different disciplines can be connected to give you insight into the way that the world works. Mental models are valuable; if you have an appreciation of compounding in finance, you can understand the implications for infection rates for viruses, something that was largely misunderstood in COVID.
Whilst scientific skills are important to the country, a good grounding in all sorts of fields is valuable to the individual; some experts believe that talking with AI as a person gets more from it and that people with humanities backgrounds can excel. It is a powerful argument; having an articulate discussion with AI is a way to get it to do more.
Critical Thinking
When spreadsheets were first introduced, there was blind faith in the print-out. This danger also applies to AI. It likes to please us and the answers it generates are echoes of the prompts used; it's easy to be fooled.
What's more, those who hold the model hold the truth, so the ability to question what you see and cross-reference it to what you know is an important skill.
Flipped Learning and Continual Challenge
Last week i mentioned that I had read Ethan Mollick's new book "Co-intelligence: Living and Working with AI". One of the things he mentions is the idea of flipped learning, where students engage at home using AI to prepare their answers, and rather than hand in a piece of coursework, they then discuss their solution in the classroom.
This is a reworking of the way we educate and it is more like the workplace; a problem-solving process that ends with a discussion. It also improves social skills and leverages the role of the tutor as a facilitator. I like it.
Something else that Ethan Mollick mentions is the need for continual challenge. It is much easier to do the same thing repeatedly and confuse it with progress. If you want to drive forward challenge yourself and make the task harder. AI is good at this as it can adjust its response to the level of the student, thereby improving the learning experience. It is truly a tiered experience. I liken it to the idea that we can all have access to an expert coach. If you haven't exercised for years, then a very moderate programme of improvement will get you on the first steps, and to improve from there means upping the difficulty to higher levels. Painful but effective.
Practical Applications
Learning in the workplace is time-consuming and expensive, for it to be genuinely helpful, it needs to apply to the circumstances employees face.
This is where GPTs come in, (custom prompts you can build in GPT4).
Any business that has important processes and procedures can assimilate them into a GPT and develop a powerful knowledge base that can be used by all.
I would urge you to give it a go.
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