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In this paper, we argue that engineering ethics education does have moral implications. More specifically, practices in engineering ethics education can lead to negative moral consequences if not conducted appropriately. Engineering ethics educators are often passionate about teaching students ways to examine the ethical implications of engineering and technology. However, ethics educators may overlook the moral significance of their instructional classroom practices. In this paper, we discuss two issues: First, we discuss the moral impacts of ethics curriculum and pedagogies on students’ learning experiences. Then we discuss the professional responsibilities of educators who are involved in designing ethics learning experiences for engineering students. The reflections presented in this paper will serve as catalysts for more comprehensive discussions regarding the impact of engineering ethics education on the ethical development of engineering students.
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Authentic Integration of Ethics and AI Through Sociotechnical, Problem-Based Learning
Growing awareness of both the demand for artificial intelligence (AI) expertise and the societal impacts of AI systems has led to calls to integrate learning of ethics alongside learning of technical skills in AI courses and pathways. In this paper, we discuss our experiences developing and piloting the TechHive AI curriculum for high school youth that integrates AI ethics and technical learning. The design of the curriculum was guided by the following pedagogical goals: (1) to respond to the capacity-building need for critical sociotechnical competencies in AI workforce pathways; and (2) to broaden participation in AI pathways through intentional instructional design to center equity in learning experiences. We provide an overview of the 30-hour learning sequence’s instructional design, and our “4D Framework,” which we use as a heuristic to help students conceptualize and inspect AI systems.We then provide a focused description of one of three chapters that make up the sequence. Finally, we present evidence of promise from an exploratory study of TechHive AI with a small sample of students, and discuss insights from implementation, including from our use of established resources for AI learning within the learning sequence as well as those created by our team.
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- Award ID(s):
- 2039637
- PAR ID:
- 10314059
- Date Published:
- Journal Name:
- Twelfth AAAI Symposium on Educational Advances in Artificial Intelligence
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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