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Artificial Intelligence in the CS2023 Undergraduate Computer Science Curriculum: Rationale and Challenges

https://doi.org/10.1609/aaai.v38i21.30352

Eaton, Eric; Epstein, Susan L (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

Roughly every decade, the ACM and IEEE professional organizations have produced recommendations for the education of undergraduate computer science students. These guidelines are used worldwide by research universities, liberal arts colleges, and community colleges. For the latest 2023 revision of the curriculum, AAAI has collaborated with ACM and IEEE to integrate artificial intelligence more broadly into this new curriculum and to address the issues it raises for students, instructors, practitioners, policy makers, and the general public. This paper describes the development process and rationale that underlie the artificial intelligence components of the CS2023 curriculum, discusses the challenges in curriculum design for such a rapidly advancing field, and examines lessons learned during this three-year process.
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CAROM Air - Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos

https://doi.org/10.1109/icra48891.2023.10160502

Lu, Duo; Eaton, Eric; Weg, Matt; Wang, Wei; Como, Steven; Wishart, Jeffrey; Yu, Hongbin; Yang, Yezhou (May 2023, IEEE)
A domain-agnostic approach for characterization of lifelong learning systems

https://doi.org/10.1016/j.neunet.2023.01.007

Baker, Megan M.; New, Alexander; Aguilar-Simon, Mario; Al-Halah, Ziad; Arnold, Sébastien M.R.; Ben-Iwhiwhu, Ese; Brna, Andrew P.; Brooks, Ethan; Brown, Ryan C.; Daniels, Zachary; et al (March 2023, Neural Networks)

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Blue sky ideas in artificial intelligence education from the EAAI 2017 new and future AI educator program

https://doi.org/10.1145/3175502.3175509

Eaton, Eric; Machado, Tiago; Williams, Tom; Koenig, Sven; Schulz, Claudia; Maurelli, Francesco; Lee, John; Eckroth, Joshua; Crowley, Mark; Freedman, Richard G.; et al (February 2018, AI Matters)

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