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The omnipresence of software systems across all aspects of society has necessitated that future technology professionals are aware of ethical concerns raised by the design and development of software and are trained to minimize harm by undertaking responsible engineering. This need has become even more urgent with artificial intelligence (AI) driven software deployment. In this paper we present a study of an interactive pedagogical intervention – role-play case studies – designed to teach undergraduate technology students about ethics with a focus on software systems. Drawing on the situated learning perspective from the Learning Sciences, we created case studies, associated stakeholder roles, discussion scripts, and pre and post discussion assignments to guide students’ learning. Open-ended data was collected from thirty-nine students and analyzed qualitatively. Findings from the study show that by taking on different perspectives on a problem, students were able to identify a range of ethical issues and understand the role of the software system process holistically, taking context, complexity, and trade-offs into account. In their discussion and reflections, students deliberated the role of software in society and the role of humans in automation. The curricula, including case studies, are publicly available for implementation.
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Can Large-Language Models Help us Better Understand and Teach the Development of Energy-Efficient Software?
Computing systems are consuming an increasing and unsustainable fraction of society’s energy footprint, notably in data centers. Meanwhile, energy-efficient software engineering techniques are often absent from undergraduate curricula. We propose to develop a learning module for energy-efficient software, suitable for incorporation into an undergraduate software engineering class. There is one major problem with such an endeavor: undergraduate curricula have limited space for mastering energy-related systems programming aspects. To address this problem, we propose to leverage the domain expertise afforded by large language models (LLMs). In our preliminary studies, we observe that LLMs can generate energy-efficient variations of basic linear algebra codes tailored to both ARM64 and AMD64 architectures, as well as unit tests and energy measurement harnesses. On toy examples suitable for classroom use, this approach reduces energy expenditure by 30–90%. These initial experiences give rise to our vision of LLM-based metacompilers as a tool for students to transform high-level algorithms into efficient, hardware-specific implementations. Complementing this tooling, we will incorporate systems thinking concepts into the learning module so that students can reason both locally and globally about the effects of energy optimizations.
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- Award ID(s):
- 2343596
- PAR ID:
- 10621251
- Publisher / Repository:
- arXiv
- Date Published:
- Subject(s) / Keyword(s):
- Large language models Energy efficiency Computing education
- Format(s):
- Medium: X
- Institution:
- PURDUE UNIVERSITY; Loyola University Chicago
- Sponsoring Org:
- National Science Foundation
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