@inproceedings{wu2024guided,
author = {Wu, Amy and Cobb, Morgan and Perez, Victor and Grant, Christan and Waisome, Jeremy A. Magruder},
title = {Guided Undergraduate Training for Shark Segmentation (GUTSS)},
year = {2024},
isbn = {9798400704246},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3626253.3635573},
doi = {10.1145/3626253.3635573},
abstract = {As artificial intelligence (AI) progresses and becomes more ubiquitously used in education, the need to provide instruction around AI skills will increase. This work presents an opportunity for students to develop image manipulation skills through segmentation. Guided Undergraduate Training for Shark Segmentation (GUTSS) is a mobile application that enables students to use these skills while simultaneously learning more about marine anatomy. To make the connection between AI and science education, in-service science teachers can use the software inside and outside of the classroom to help students learn about different aspects of shark anatomy through the GUTSS. Through the application, teachers can enrich their classroom curriculum with technology, share materials, grade assignments, and view their students' work. GUTSS uses open-set object detection, image segmentation, and image manipulation to assist users with organ identification. Gamification within the application will make learning shark anatomy more engaging to students. Prior work from the NSF ITEST Award indicates teachers' willingness to integrate AI concepts into the classroom aligned with state standards. Further wireframing of the application from instructor perspectives is ongoing, to incorporate teacher viewpoints from mixed-methods survey responses on AI usage and anatomy. Future work aims to supplement shark dissection images from AI generation of anatomical image data sets, by collecting these data from students and teachers. Ultimately, the application will be able to identify non-aquatic organisms' anatomical features as a tool for students to learn.},
booktitle = {Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 2},
pages = {1857–1858},
numpages = {2},
keywords = {ai, anatomy, education, gamification, ml, science, segmentation},
location = {<conf-loc>, <city>Portland</city>, <state>OR</state>, <country>USA</country>, </conf-loc>},
series = {SIGCSE 2024}
}