research-article

How ChatGPT Will Change Software Engineering Education

Authors:

Jennifer BringsAuthors Info & Claims

ITiCSE 2023: Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1

Pages 110 - 116

https://doi.org/10.1145/3587102.3588815

Published: 30 June 2023 Publication History

Abstract

This position paper discusses the potential for using generative AIs like ChatGPT in software engineering education. Currently, discussions center around potential threats emerging from student's use of ChatGPT. For instance, generative AI will limit the usefulness of graded homework dramatically. However, there exist potential opportunities as well. For example, ChatGPT's ability to understand and generate human language allows providing personalized feedback to students, and can thus accompany current software engineering education approaches. This paper highlights the potential for enhancing software engineering education. The availability of generative AI will improve the individualization of education approaches. In addition, we discuss the need to adapt software engineering curricula to the changed profiles of software engineers. Moreover, we point out why it is important to provide guidance for using generative AI and, thus, integrate it in courses rather than accepting the unsupervised use by students, which can negatively impact the students' learning.

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Cited By

Maguire JEnglish RCao QSeow C(2025)Themes in the Declared Use of Generative Artificial Intelligence in AssessmentProceedings of the 9th Conference on Computing Education Practice10.1145/3702212.3702217(17-20)Online publication date: 7-Jan-2025
https://dl.acm.org/doi/10.1145/3702212.3702217
Cano-Marin E(2024)transformative potential of Generative Artificial Intelligence (GenAI) in businessESIC Market10.7200/esicm.55.33355:2(e333)Online publication date: 31-May-2024
https://doi.org/10.7200/esicm.55.333
Filippi SMotyl B(2024)Large Language Models (LLMs) in Engineering Education: A Systematic Review and Suggestions for Practical AdoptionInformation10.3390/info1506034515:6(345)Online publication date: 12-Jun-2024
https://doi.org/10.3390/info15060345
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Index Terms

How ChatGPT Will Change Software Engineering Education
1. Applied computing
  1. Education
2. Software and its engineering

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cover image ACM Conferences

ITiCSE 2023: Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1

June 2023

694 pages

ISBN:9798400701382

DOI:10.1145/3587102

General Chairs:
Mikko-Jussi Laakso
University of Turku, Finland
,
Mattia Monga
University of Milan, Italy
,
Program Chairs:
Simon
Unaffiliated, Australia
,
Judithe Sheard
Monash University, Australia

Copyright © 2023 ACM.

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Published: 30 June 2023

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ITiCSE 2023: Innovation and Technology in Computer Science Education

July 7 - 12, 2023

Turku, Finland

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Cited By

Maguire JEnglish RCao QSeow C(2025)Themes in the Declared Use of Generative Artificial Intelligence in AssessmentProceedings of the 9th Conference on Computing Education Practice10.1145/3702212.3702217(17-20)Online publication date: 7-Jan-2025
https://dl.acm.org/doi/10.1145/3702212.3702217
Cano-Marin E(2024)transformative potential of Generative Artificial Intelligence (GenAI) in businessESIC Market10.7200/esicm.55.33355:2(e333)Online publication date: 31-May-2024
https://doi.org/10.7200/esicm.55.333
Filippi SMotyl B(2024)Large Language Models (LLMs) in Engineering Education: A Systematic Review and Suggestions for Practical AdoptionInformation10.3390/info1506034515:6(345)Online publication date: 12-Jun-2024
https://doi.org/10.3390/info15060345
Kotsiantis SVerykios VTzagarakis M(2024)AI-Assisted Programming Tasks Using Code Embeddings and TransformersElectronics10.3390/electronics1304076713:4(767)Online publication date: 15-Feb-2024
https://doi.org/10.3390/electronics13040767
Ogunleye BZakariyyah KAjao OOlayinka OSharma H(2024)A Systematic Review of Generative AI for Teaching and Learning PracticeEducation Sciences10.3390/educsci1406063614:6(636)Online publication date: 13-Jun-2024
https://doi.org/10.3390/educsci14060636
Sampaio SLima Mde Souza EMeireles MPessoa MConte T(2024)Exploring the Use of Large Language Models in Requirements Engineering Education: An Experience Report with ChatGPT 3.5Proceedings of the XXIII Brazilian Symposium on Software Quality10.1145/3701625.3701687(624-634)Online publication date: 5-Nov-2024
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Keuning HAlpizar-Chacon ILykourentzou IBeehler LKöppe Cde Jong ISosnovsky S(2024)Students' Perceptions and Use of Generative AI Tools for Programming Across Different Computing CoursesProceedings of the 24th Koli Calling International Conference on Computing Education Research10.1145/3699538.3699546(1-12)Online publication date: 12-Nov-2024
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Korpimies KLaaksonen ALuukkainen M(2024)Unrestricted Use of LLMs in a Software Project Course: Student Perceptions on Learning and Impact on Course PerformanceProceedings of the 24th Koli Calling International Conference on Computing Education Research10.1145/3699538.3699541(1-7)Online publication date: 12-Nov-2024
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Jiang ZWen MCao JShi XJin HFilkov VRay BZhou M(2024)Towards Understanding the Effectiveness of Large Language Models on Directed Test Input GenerationProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695513(1408-1420)Online publication date: 27-Oct-2024
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Servin CKarichev NPagel M(2024)Unfolding Programming: How to Use AI Tools in Introductory Computing CoursesProceedings of the 25th Annual Conference on Information Technology Education10.1145/3686852.3687073(49-55)Online publication date: 10-Oct-2024
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