In his book, “Make. Think. Imagine”, John Browne writes: “Artificial intelligence seems to be everywhere”. Some of its recent applications and achievements are well known; its potential to help build – or undermine – our civilisation is immense. As teachers, it’s hard for us to ignore this potential. AI is changing the tools we have at our disposal and adds to the impetus for a curriculum which incorporates the so-called "21st century skills". In this module, you will think about how AI may be defined, particularly in relation to human intelligence, and how it has taken different forms. You will consider how it has evolved over the last 70 years and come to show promise in so many different applications.

Both design thinking, and critical and creative thinking (CCT), embrace skills which are important for teams working to solve problems with AI. In design thinking, these skills include collaboration, solving a problem with constraints, coming up with an idea, testing it, and if necessary, going back to the drawing board. The process is iterative. As for CCT, creative thinking is required to identify opportunities for AI, and critical thinking can help us evaluate its application. In this module, you will learn about the key principles and stages in the design thinking process. You will consider CCT from the perspective of convergent and divergent thinking, de Bono's Six Hats, and ACARA's four quadrant model. Examples of "creative AI" will be examined to help us understand the barriers to machine creativity.

When it comes to computational thinking skills, there are two people who’ve been very influential in bringing them to the classroom. One is Seymour Papert who in the 1960s developed a computer program for children. The other influential person is Jeanette Wing, a data scientist, who wrote a short article advocating the use of computational thinking. In this module, you’ll appreciate that human or AI decision-making depends crucially on the quality of data. You’ll think about what data is, how it takes different forms and can be represented in different ways. Using the website, Machine Learning for Kids, we will look at what has been a classic problem in image recognition: being able to distinguish between cats and dogs. You’ll also learn about just what computational thinking is, its place in the Australian Curriculum, and the ideas and skills behind it. You’ll see how these skills can be developed with both “unplugged” and computer-based activities.

Back in the 1940s, one of the grand masters of science fiction, Isaac Asimov, introduced into his stories the "laws of robotics" which were designed to protect humans from wayward machines. While machine intelligence has not evolved in ways that Asimov could have foreseen, there is still a need to ensure that AI works for the good of society. In this module, we will explore some of the ethical issues associated with AI. We will look at a CSIRO framework to help deal with these issues. We will see that decisions or predictions based on AI can be just as biased and prejudicial as those made by human individuals and groups. The Human Rights Commission recently reported the results of a poll designed to garner support for a variety of protective measures (Hunter, F. 'Human Rights Commission warns government over “dangerous” use of AI.' SMH, August 15, 2020). For example, it might be considered important that people should know when a decision which affects them, has been made by computer and that they have the right to appeal against such a decision.

In this module we'll summarise the course, and remind you of some options you might like to utilise in your future teaching practice.