How can large language models and prompt engineering be leveraged in Computer Science education?

Systematic literature review

Bachelor thesis (2023)

Authors

Alexandra Neagu 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 Alexandra Neagu
Natural Language Processing (NLP) Education Large Language Models (LLMs) BERT Code generation Prompt engineering ChatGPT
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Published Date

28-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

In recent years, significant progress has been made in the field of natural language processing (NLP) through the development of large language models (LLMs) like BERT and ChatGPT. These models have showcased remarkable abilities across a range of NLP tasks. However, effectively harnessing their potential requires meticulous prompt engineering and a comprehensive understanding of their limitations.

Additionally, LLMs have attracted attention in the educational domain for their potential to enhance learning and teaching experiences, particularly in fostering the development of computational thinking skills.
This paper aims to explore the potential of leveraging NLP and prompt engineering techniques to generate successful solutions to coding problems following initial failures. Furthermore, the research explores the potential applications of NLP techniques in teaching and learning practices involving LLMs and their potential drawbacks in this context.