Artificial Intelligence for Teachers

This course is a general introduction to artificial intelligence, designed for the students on the teacher tracks in sciences. We focus on clarifying what artificial intelligence is, on what can be done with artificial intelligence, and on various societal concerns around the wide adoption of artificial intelligence. The course aims to provide an exploration of the nature of the problems addressed in artificial intelligence and of the computational strategies behind the most popular approaches in this field. The topics we cover will include design and analysis of machine learning experiments, training a model and using it for predictions. We will discuss regression, classification and deep learning. Short assignments include hands-on experiments with various learning algorithms. We will also have an active discussion on ethical aspects of AI and on ideas of how short lectures on AI could be offered in school classrooms. The course will consist of several components: • Lectures. The lectures will be pre-recorded and continuously available online. They will consist of short (typically 10-20 min) videos focused on a single concept, example, or technique. Having them available continuously is key to the success of this course. The students may return to study various blocks freely, even after the course finishes. • Hands-on projects. Experimenting directly with how AI works is an important part of this course. The students will go through 3 projects, each on a different AI approach (e.g., regression, classification, and deep learning). Each project will be available in the form of a “Jupyter notebook” that will have two parts. The first will be a tutorial, where the students can simply run the code provided in the tutorial (but can also edit it and experiment with it) to learn the technique underlying that project. The second will be a “challenge” where the student will fill in a code template provided in the project, on the basis of the tutorial in the first part of the project. The coding challenge will be minor, the accent will rather be on seeing which part of the code from the tutorial needs to be slightly adapted to make it answer to a clearly formulated question. Jupyter notebooks can be edited and run in a standard browser, using freely available tools such as Google Collaboratory. Special attention will be given to prevent the coding from becoming a bottleneck in studying this course. • Plan an AI classroom lecture. Each student will make a plan for how to give a 2h-lecture on AI in the classroom. They will be subject to peer-review between the students and active discussions between them. • Societal aspects of AI. Each student will write a short essay on societal aspects of AI. The essays will be peer-reviewed between the students and actively discussed. The topic can be freely chosen by each student from the social, societal, and even philosophical dimensions of using AI widely. This may include ethics, safety, security, liability, trust, privacy, job market transformations, or even, the ultimate threat caused by “singular” self-aware AI exceeding the cognitive performance of humans. The video lectures will be available in English, the discussions and the essays can be in Finnish.