Student Perspectives on Using a Large Language Model (LLM) for an Assignment on Professional Ethics

Grande, Virginia; Kiesler, Natalie; Francisco R., María Andreína

doi:10.1145/3649217.3653624

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Other2

Article2

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

With Great Power Comes Great Responsibility - Integrating Data Ethics into Computing Education

Kiesler,

Opel,

Thorbrügge

2024

Proceedings of the 2024 on Innovation and Technology in Computer Science Education v. 1

View full text Add to dashboard Cite

With Great Power Comes Great Responsibility - Integrating Data Ethics into Computing Education

Kiesler,

Opel,

Thorbrügge

2024

Proceedings of the 2024 on Innovation and Technology in Computer Science Education v. 1

View full text Add to dashboard Cite

Novice Learners of Programming and Generative AI - Prior Knowledge Matters

Kiesler,

Scholz,

Albrecht

et al. 2024

Proceedings of the 24th Koli Calling International Conference on Computing Education Research

View full text Add to dashboard Cite

Risk management strategy for generative AI in computing education: how to handle the strengths, weaknesses, opportunities, and threats?

Humble

2024

Int J Educ Technol High Educ

View full text Add to dashboard Cite

The idea of Artificial intelligence (AI) has a long history in both research and fiction and has been applied in educational settings since the 1970s. However, the topic of AI underwent a huge increase of interest with the release of ChatGPT in late 2022, and more people were talking about generative AI (GenAI or GAI). According to some estimates, the number of publications on generative AI increased with 2269.49% between 2022 and 2023, and the increase was even higher when related to computing education. The aim of this study is to investigate the potential strengths, weaknesses, opportunities, and threats of generative AI in computing education, as highlighted by research published after the release of ChatGPT. The study applied a scoping literature review approach with a three-step process for identifying and including a total of 129 relevant research papers, published in 2023 and 2024, through the Web of Science and Scopus databases. Included papers were then analyzed with a theoretical thematic analysis, supported by the SWOT analysis framework, to identify themes of strengths, weaknesses, opportunities, and threats with generative AI for computing education. A total of 19 themes were identified through the analysis. Findings of the study have both theoretical and practical implications for computing education specifically, and higher education in general. Findings highlights several challenges posed by generative AI, such as potential biases, overreliance, and loss of skills; but also several possibilities, such as increasing motivation, educational transformation, and supporting teaching and learning. The study expands the traditional SWOT analysis, by providing a risk management strategy for handling the strengths, weaknesses, opportunities, and threats of generative AI.

show abstract

Student Perspectives on Using a Large Language Model (LLM) for an Assignment on Professional Ethics

Cited by 4 publications

References 26 publications

With Great Power Comes Great Responsibility - Integrating Data Ethics into Computing Education

With Great Power Comes Great Responsibility - Integrating Data Ethics into Computing Education

Novice Learners of Programming and Generative AI - Prior Knowledge Matters

Risk management strategy for generative AI in computing education: how to handle the strengths, weaknesses, opportunities, and threats?

Contact Info

Product

Resources

About