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Multiple studies call for engineering education to integrate social justice into classroom instruction. Yet, there is uncertainty regarding whether integrating these social topics into engineering curriculum will support or detract from the learning of technical concepts. This study focuses on evaluating how reframing technical assessments to include social justice concepts impacts student learning and investigates how well students integrate social justice into engineering decision making. Using a within-subject design, in which students were exposed to both conditions (questions with and without social justice context), we evaluate how social justice framing impacts overall student learning of technical topics. Social justice prompts are added to homework questions, and we assess students’ demonstration of knowledge of original technical content of the course, as well as their ability to consider social justice implications of engineering design. In the earlier homework assignment, the experimental group showed a significant decrease in learning when technical concepts were framed to include social justice. As the students became more familiar with social justice considerations, their learning of technical concepts became comparable to that of students who did not have the social justice components in their assignment. Their evaluation of the social implications of technical decisions also improved. History: This paper has been accepted for the INFORMS Transactions on Education Special Issue on DEI in ORMS Classrooms. Funding: This work was supported by the Carnegie Mellon University’s Wimmer Faculty Fellowship and the National Science Foundation [Grant 2053856]. D. Nock also acknowledges support from the Wilton E. Scott Institute for Energy Innovation, where she is an energy fellow.
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The Shortest Path to Ethics in AI: An Integrated Assignment Where Human Concerns Guide Technical Decisions
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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- Award ID(s):
- 2041960
- PAR ID:
- 10354617
- Date Published:
- Journal Name:
- ICER '22: Proceedings of the 2022 ACM Conference on International Computing Education Research
- Volume:
- 1
- Page Range / eLocation ID:
- 344 to 355
- 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/10.1145/3501385.3543978
