The idea of computational thinking has been used to denote set of skills that should be promoted as part of a broad education. The term originates with work by Jeanette Wing (e.g. this CACM article) over a decade ago. Computational thinking has developed to mean two, slightly different things. Firstly, the use of ideas coming out of computing for a wide variety of tasks, not always concerned with implementing solutions on computers. Systematic descriptions of processes, clear descriptions of data, ideas of data types, etc. are seen as valuable mental concepts for everyone to learn and apply. As a pithy but perhaps rather tone-deaf saying has it: “coding is the new Latin”.
A second, related, meaning is the kinds of thinking required to convert a complex real-world problem into something that can be solved on a computer. This requires a good knowledge of coding and the capabilities of computer systems, but is isn’t exactly the coding process as such: it is the process required to get to the point where the task is obvious to an experienced coder. These are the kind of tasks that are found in the Bebras problem sets, for example. We have found these very effective in checking whether people have the skills in abstraction and systematisation that are needed before attempting to learn to code; they test the kinds of things that are needed in computational thinking without requiring actual computing knowledge.
A thought that occurred to me today is that these problems provide a really good challenge for artificial intelligence. Despite being described as “computational thinking” problems, they are actually problems that test the kind of things that computers cannot do—the interstitial material between the messy real world and the structured problems that can be tackled by computer. This makes them exactly the sort of things that AI ought to be working towards and where we could gain lots of insight about intelligence. One promising approach is the “mind’s eye” visual manipulation described by Maithilee Kunda in this paper about visual mental imagery and AI.