Build Your Own Robot Friend: An Open-Source Learning Module for
Accessible and Engaging AI Education
- URL: http://arxiv.org/abs/2402.01647v1
- Date: Sat, 6 Jan 2024 08:03:08 GMT
- Title: Build Your Own Robot Friend: An Open-Source Learning Module for
Accessible and Engaging AI Education
- Authors: Zhonghao Shi, Allison O'Connell, Zongjian Li, Siqi Liu, Jennifer
Ayissi, Guy Hoffman, Mohammad Soleymani, Maja J. Matari\'c
- Abstract summary: We developed an open-source learning module for college and high school students, which allows students to build their own robot companion.
This open platform can be used to provide hands-on experience and introductory knowledge about various aspects of AI.
Because of the social and personal nature of a socially assistive robot companion, this module also puts a special emphasis on human-centered AI.
- Score: 10.864182981901271
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: As artificial intelligence (AI) is playing an increasingly important role in
our society and global economy, AI education and literacy have become necessary
components in college and K-12 education to prepare students for an AI-powered
society. However, current AI curricula have not yet been made accessible and
engaging enough for students and schools from all socio-economic backgrounds
with different educational goals. In this work, we developed an open-source
learning module for college and high school students, which allows students to
build their own robot companion from the ground up. This open platform can be
used to provide hands-on experience and introductory knowledge about various
aspects of AI, including robotics, machine learning (ML), software engineering,
and mechanical engineering. Because of the social and personal nature of a
socially assistive robot companion, this module also puts a special emphasis on
human-centered AI, enabling students to develop a better understanding of
human-AI interaction and AI ethics through hands-on learning activities. With
open-source documentation, assembling manuals and affordable materials,
students from different socio-economic backgrounds can personalize their
learning experience based on their individual educational goals. To evaluate
the student-perceived quality of our module, we conducted a usability testing
workshop with 15 college students recruited from a minority-serving
institution. Our results indicate that our AI module is effective,
easy-to-follow, and engaging, and it increases student interest in studying
AI/ML and robotics in the future. We hope that this work will contribute toward
accessible and engaging AI education in human-AI interaction for college and
high school students.
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