Summary of A Survey Study on the State Of the Art Of Programming Exercise Generation Using Large Language Models, by Eduard Frankford et al.
A Survey Study on the State of the Art of Programming Exercise Generation using Large Language Models
by Eduard Frankford, Ingo Höhn, Clemens Sauerwein, Ruth Breu
First submitted to arxiv on: 30 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The research explores the capabilities of Large Language Models (LLMs) in generating programming exercises. A survey study identifies the state-of-the-art, strengths, and weaknesses of various LLMs, providing an evaluation matrix for researchers and educators to select the most suitable model for this task. The study reveals that multiple LLMs can produce useful programming exercises, but also highlights challenges such as the ease with which LLMs might solve exercises generated by themselves. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well Large Language Models (LLMs) can create programming exercises. Scientists studied many different LLMs to see what they’re good at and what they struggle with. They came up with a way to compare these models, so teachers and researchers can pick the best one for making programming exercises. The study shows that some LLMs are really good at creating helpful exercises, but it also points out that these models might be too smart for their own good – they could solve the exercises they create too easily! This research helps us think about how to use these powerful tools in education. |
