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

How can large language models and prompt engineering be leveraged in Computer Science education?: Systematic literature review

Neagu, Alexandra (TU Delft Electrical Engineering, Mathematics and Computer Science)

Aivaloglou, E.A. (mentor)
Zhang, X. (mentor)
Viering, T.J. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-06-28

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.

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

http://resolver.tudelft.nl/uuid:f2d50c24-6f10-4f82-9f69-7edff5ea44ba

Student theses

bachelor thesis

© 2023 Alexandra Neagu