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https://doi.org/10.1109/cseet6...
Article . 2024 . Peer-reviewed
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Article . 2024
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A Survey Study on the State of the Art of Programming Exercise Generation Using Large Language Models

Authors: Frankford, Eduard; Höhn, Ingo; Sauerwein, Clemens; Breu, Ruth;

A Survey Study on the State of the Art of Programming Exercise Generation Using Large Language Models

Abstract

This paper analyzes Large Language Models (LLMs) with regard to their programming exercise generation capabilities. Through a survey study, we defined the state of the art, extracted their strengths and weaknesses and finally proposed an evaluation matrix, helping researchers and educators to decide which LLM is the best fitting for the programming exercise generation use case. We also found that multiple LLMs are capable of producing useful programming exercises. Nevertheless, there exist challenges like the ease with which LLMs might solve exercises generated by LLMs. This paper contributes to the ongoing discourse on the integration of LLMs in education.

5 pages, 0 figures, CSEE&T 2024

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Keywords

Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green