Artificial intelligence (AI) and its teaching in the K-12 grades has been championed as a vital need for the United States due to the technology's future prominence in the 21st century. However, there remain several barriers to effective AI lessons at these age groups including the broad range of interdisciplinary knowledge needed and the lack of formal training or preparation for teachers to implement these lessons. In this experience report, we present ImageSTEAM, a teacher professional development for creating lessons surrounding computer vision, machine learning, and computational photography/cameras targeted for middle school grades 6-8 classes. Teacher professional development workshops were conducted in the states of Arizona and Georgia from 2021-2023 where lessons were co-created with teachers to introduce various specific visual computing concepts while aligning to state and national standards. In addition, the use of a variety of computer vision and image processing software including custom designed Python notebooks were created as technology activities and demonstrations to be used in the classroom. Educational research showed that teachers improved their self-efficacy and outcomes for concepts in computer vision, machine learning, and artificial intelligence when participating in the program. Results from the professional development workshops highlight key opportunities and challenges in integrating this content into the standard curriculum, the benefits of a co-creation pedagogy, and the positive impact on teacher and student's learning experiences. The open-source program curriculum is available at www.imagesteam.org.
Data Standards for Artificial Life Software
As the field of Artificial Life advances and grows, we find ourselves in the midst of an increasingly complex ecosystem of software systems. Each system is developed to address particular research objectives, all unified under the common goal of understanding life. Such an ambitious endeavor begets a variety of algorithmic challenges. Many projects have solved some of these problems for individual systems, but these solutions are rarely portable and often must be re-engineered across systems. Here, we propose a community-driven process of developing standards for representing commonly used types of data across our field. These standards will improve software re-use across research groups and allow for easier comparisons of results generated with different artificial life systems. We began the process of developing data standards with two discussion-driven workshops (one at the 2018 Conference for Artificial Life and the other at the 2018 Congress for the BEACON Center for the Study of Evolution in Action). At each of these workshops, we discussed the vision for Artificial Life data standards, proposed and refined a standard for phylogeny (ancestry tree) data, and solicited feedback from attendees. In addition to proposing a general vision and framework for Artificial Life data standards, we release and discuss version 1.0.0 of the standards. This release includes the phylogeny data standard developed at these workshops and several software resources under development to support our proposed phylogeny standards framework.
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- Award ID(s):
- 1655715
- PAR ID:
- 10308966
- Date Published:
- Journal Name:
- The 2019 Conference on Artificial Life
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1162/isal_a_00213
