6.S898 Democratizing AI through K-12 AI Education for All

SHARE:

Graduate Level (Also listed as MAS.S65)
Units: 2-3-7
Prereqs: Permission of Instructor
Instructors:  Professors Hal Abelson and Cynthia Breazeal
Schedule:   W2-4 room E14-493
 
Overview:
 
How can we prepare 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 middle-school students to learn about AI in a collaborative, hands-on way?” Students taking this course will work in teams to develop constructionist tools and activities to introduce 6th-8th grade 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. Homework will involve critiques of existing project-based learning curriculum and design of new curriculum modules. Fieldwork will involve working with middle-school students in afterschool  programs to evaluate hands-on projects in an iterative cycle of development and refinement with stakeholder input. An important objective of class projects is to effectively integrate ethical design concepts and practices into proposed activities and curriculum in grade-appropriate ways so that students appreciate these issues in the AI-enabled projects they create.
 
Example class projects can take the form of developing an AI curriculum module that covers a core AI concept through hands-on projects based on Scratch or App Inventor with integrated services (machine learning, computer vision, natural language understanding, etc.).
 
Existing research projects could be translated into compelling hands-on projects that introduce students to exciting AI methods and abilities. Other projects could explore include developing  new AI capabilities for mobile devices, training teachers and mentors to support students as they learn about AI, and developing AI-based mentoring agents to help scale this knowledge and training.
 

Grading
20%     homework: design reviews and critiques
20%     class attendance and participation
20%     field work: working with students in schools
40%     final project (no exam)