Student Perspectives on Using a Large Language Model (LLM) for an Assignment on Professional Ethics
<|reference_start|>Student Perspectives on Using a Large Language Model (LLM) for an Assignment on Professional Ethics: The advent of Large Language Models (LLMs) started a serious discussion among educators on how LLMs would affect, e.g., curricula, assessments, and students' competencies. Generative AI and LLMs also raised ethical questions and concerns for computing educators and professionals. This experience report presents an assignment within a course on professional competencies, including some related to ethics, that computing master's students need in their careers. For the assignment, student groups discussed the ethical process by Lennerfors et al. by analyzing a case: a fictional researcher considers whether to attend the real CHI 2024 conference in Hawaii. The tasks were (1) to participate in in-class discussions on the case, (2) to use an LLM of their choice as a discussion partner for said case, and (3) to document both discussions, reflecting on their use of the LLM. Students reported positive experiences with the LLM as a way to increase their knowledge and understanding, although some identified limitations. The LLM provided a wider set of options for action in the studied case, including unfeasible ones. The LLM would not select a course of action, so students had to choose themselves, which they saw as coherent. From the educators' perspective, there is a need for more instruction for students using LLMs: some students did not perceive the tools as such but rather as an authoritative knowledge base. Therefore, this work has implications for educators considering the use of LLMs as discussion partners or tools to practice critical thinking, especially in computing ethics education.<|reference_end|>
@article{grande2024student,
    title={Student Perspectives on Using a Large Language Model (LLM) for an
  Assignment on Professional Ethics},
    author={Virginia Grande, Natalie Kiesler and Maria Andreina Francisco R},
    journal={arXiv preprint arXiv:2406.11858},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11858},
    primaryClass={cs.CY}
}