Use of AI-driven code generation models in teaching and learning programming

a systematic literature review

Bachelor thesis (2023)

Authors

D. Cambaz Electrical Engineering, Mathematics and Computer Science

Contributors

E.A. Aivaloglou Web Information Systems - EEMCS (supervisor 1)
X. Zhang Web Information Systems - EEMCS (supervisor 1)
T.J. Viering Pattern Recognition and Bioinformatics - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Doga Cambaz
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Published Date

28-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The recent emergence of AI-driven code generation models can potentially transform programming education. To pinpoint the current state of research on using AI code generators to support learning and teaching programming, we conducted a systematic literature review with 21 papers published since 2018. The review presents the teaching and learning practices in programming education that utilize these models, the characteristics and performance indicators of the code generation models, and aspects to be considered when utilizing the models in programming education, including the risks and challenges of using code generation models for educational practices. AI code generators can be an assistive tool for both learners and instructors if the risks are mitigated.