Schedule: Lectures Mondays 3-5, room E14-493
Instructors: Professors Hal Abelson and Cynthia Breazeal
Enrollment limited: 12 Permission of instructor only
How can we prepare non-university students with knowledge, skills, and attitudes for future careers that increasingly rely on AI technologies? Otherwise, we risk leaving far too many people behind in the emerging AI-economy -- causing significant societal stress and divisiveness rather than enabling transformative opportunity where everyone can participate in, benefit from, influence our future with AI.
Inequity of education remains a key barrier to future opportunities and jobs where success depends increasingly on intellect, creativity, and the right skills. While AI is already entering the education system to support students, teachers, or school administration -- it is not currently offered as a topic to be learned until the university level. Just as learning to code has become recognized as a new literacy for the 21st century, students need to also learn about AI given its growing prevalence across industries, institutions, and society on a global scale.
This weekly project-based class explores the question of “how do we empower children, from preschool to high school, to learn about AI in a collaborative, hands-on way?” Students taking this course will collaborate in teams to develop constructionist tools and activities to introduce preK-12 learners to important concepts, practices and design principles of artificial intelligence – i.e., how machines think and learn and how to design them in an ethical way. An important objective of class projects is to effectively integrate ethical design concepts and practices into their proposed activities and curriculum so that preK-12 students appreciate issues in bias, fairness, transparency, etc. in the AI-enabled projects they create in an age appropriate way. Example projects can take the form of developing an AI curriculum module that covers a core AI concept and associated practices through hands-on projects based on scratch, app inventor, Jupytr notebooks, etc. with integrated cognitive services or open source libraries (machine learning, computer vision, NLU, etc.). Existing research projects could be translated into compelling hands-on projects that introduce younger students to exciting AI methods and abilities. Other projects can explore how to effectively prepare and train mentors to support students as they learn about AI, including the development of personalized AI mentoring agents to help scale this knowledge and training.
40% design reviews and critiques
20% class attendance and participation
40% final project (no exam)