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How can we teach AI students to use human concerns to guide their technical decisions? We created an AI assignment with a human context, asking students to find the safest path rather than the shortest path. This integrated assignment evaluated 120 students’ understanding of the limitations and assumptions of standard graph search algorithms, and required students to consider human impacts to propose appropriate modifications. Since the assignment focused on algorithm selection and modification, it provided the instructor with a different perspective on student understanding (compared with questions on algorithm execution). Specifically, many students: tried to solve a bottleneck problem with algorithms designed for accumulation problems, did not distinguish between calculations that could be done during the incremental construction of a path versus ones that required knowledge of the full path, and, when proposing modifications to a standard algorithm, did not present the full technical details necessary to implement their ideas. We created rubrics to analyze students’ responses. Our rubrics cover three dimensions: technical AI knowledge, consideration of human factors, and the integration of technical decisions as they align with the human context. These rubrics demonstrate how students’ skills can vary along each dimension, and also provide a template for scoring integrated assignments for other CS topics. Overall, this work demonstrates how to integrate human concerns with technical content in a way that deepens technical rigor and supports instructor pedagogical content knowledge.
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Model AI Assignments 2020
The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of nine AI assignments from the 2020 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.
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- Award ID(s):
- 1724392
- PAR ID:
- 10179966
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Symposium on Educational Advances in Artificial Intelligence (EAAI)
- 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/10.1609/aaai.v34i09.7072
