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Guided Undergraduate Training for Shark Segmentation (GUTSS)

Published: 15 March 2024 Publication History

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.

References

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Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick. 2023. Segment Anything. arXiv:2304.02643 (2023).
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Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, et al. 2023. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. arXiv preprint arXiv:2303.05499 (2023).
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Mai Neo, Chin Poo Lee, Heidi Yeen Ju Tan, Tse Kian Neo, Yong Xuan Tan, Nazi Mahendru, and Zahra Ismat. 2022. Enhancing Students' Online Learning Experiences with Artificial Intelligence (AI): The MERLIN Project. International Journal of Technology 13, 5 (2022), 1023.
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cover image ACM Conferences
SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 2
March 2024
2007 pages
ISBN:9798400704246
DOI:10.1145/3626253
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 15 March 2024

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Author Tags

  1. ai
  2. anatomy
  3. education
  4. gamification
  5. ml
  6. science
  7. segmentation

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