Computing Education in the Era of Generative AI-Reference-Cited by-同舟云学术

Computing Education in the Era of Generative AI

Published:2024-01-25 Issue:2 Volume:67 Page:56-67
ISSN:0001-0782
Container-title:Communications of the ACM
language:en
Short-container-title:Commun. ACM

Author:

Denny Paul¹,Prather James²,Becker Brett A.³,Finnie-Ansley James¹,Hellas Arto⁴,Leinonen Juho¹,Luxton-Reilly Andrew¹,Reeves Brent N.²,Santos Eddie Antonio³,Sarsa Sami⁴

Affiliation:

1. University of Auckland, Auckland, New Zealand

2. Abilene Christian University, Abilene, TX, USA

3. University College Dublin, Dublin, Ireland

4. Aalto University, Espoo, Finland

Abstract

Challenges and opportunities faced by computing educators and students adapting to LLMs capable of generating accurate source code from natural-language problem descriptions.

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/3624720

Reference43 articles.

1. Grounded Copilot: How programmers interact with code-generating models;Barke S.;Proceedings of the ACM on Programming Languages,2022

2. Becker, B.A. et al. Programming is hard -- Or at least it used to be: Educational opportunities and challenges of AI code generation. In Proceedings of the 54th SIGCSE Technical Symp. on Computer Science Education 1, Association for Computing Machinery (2023), 500--506.

3. Fooling MOSS Detection with Pretrained Language Models

4. Bommasani R. et al. On the opportunities and risks of foundation models 10.48550/ARXIV.2108.07258

5. Chen M. et al. Evaluating large language models trained on code. (2021); https://arxiv.org/abs/2107.03374.

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