I’m speaking to corporate leaders later this week about strategic approaches to AI in business. This is one of my slides where I’ve further refined my definition of AI literacy based on my Gen AI coaching experiences since 2023.
On the left side are the basics – knowing AI (the different types, how they work), when to use AI (to automate or not?), and how to use AI (prompting techniques, use of different AI apps).
On the right side are the advanced stuff – applying human agency (intention, knowledge, nuance), evaluating the AI output (which requires expertise and wisdom), and building own tools such as chatbots and web apps, a relatively new phenomenon.
In the middle is a word that I’m still not comfortable with using, because it’s so scary-sounding to most of us: “Computational Thinking”.
Computational thinking is a way of thinking that borrows techniques and thought processes from computer science to break down complex problems into smaller, more manageable parts. This enables individuals to tackle challenges in a structured and logical manner, not just in the context of computer science but across various disciplines.
Core concepts in computational thinking:
🔷 Decomposition: Breaking down a complex problem into smaller, more manageable sub-problems.
🔷 Pattern Recognition: Identifying patterns and trends within data or problems to find similarities and differences.
🔷 Abstraction: Focusing on the essential information while ignoring irrelevant details.
🔷 Algorithmic Thinking: Developing step-by-step instructions (an algorithm) to solve a problem.
For people who are problem solvers, all these are familiar steps. However, I think we need a better term for this, because many people don’t like to be compared to machines.