The Implications of Large Language Models for CS Teachers and Student

MacNeil, Stephen; Kim, Joanne; Leinonen, Juho; Denny, Paul; Bernstein, Seth; Becker, Brett A.; Wermelinger, Michel; Hellas, Arto; Tran, Andrew; Sarsa, Sami; Prather, James and Kumar, Viraj (2023). The Implications of Large Language Models for CS Teachers and Student. In: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2, ACM p. 1255.

DOI: https://doi.org/10.1145/3545947.3573358

Abstract

The introduction of Large Language Models (LLMs) has generated a significant amount of excitement both in industry and among researchers. Recently, tools that leverage LLMs have made their way into the classroom where they help students generate code and help instructors generate learning materials. There are likely many more uses of these tools -- both beneficial to learning and possibly detrimental to learning. To help ensure that these tools are used to enhance learning, educators need to not only be familiar with these tools, but with their use and potential misuse. The goal of this BoF is to raise awareness about LLMs and to build a learning community around their use in computing education. Aligned with this goal of building an inclusive learning community, our BoF is led by globally distributed discussion leaders, including undergraduate researchers, to facilitate multiple coordinated discussions that can lead to a broader conversation about the role of LLMs in CS education.

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