Summary of Large Language Models in Computer Science Education: a Systematic Literature Review, by Nishat Raihan et al.
Large Language Models in Computer Science Education: A Systematic Literature Review
by Nishat Raihan, Mohammed Latif Siddiq, Joanna C.S. Santos, Marcos Zampieri
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC)
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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 paper presents a systematic literature review examining the impact of Large Language Models (LLMs) on computer science and engineering education. Foundational models like GPT and LLaMA have achieved strong baseline performances in various Natural Language Processing (NLP) and programming language tasks, with fine-tuned models showing significant improvements in code generation. Both foundational and fine-tuned models are increasingly used to support students’ writing, debugging, and understanding of code. The study aims to analyze the effectiveness of LLMs in enhancing learning experiences, supporting personalized education, and aiding educators in curriculum development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how big language models can help teach computer science and engineering. These models are really good at tasks like writing and understanding natural language, and they’re also getting better at programming languages. Some models have even been fine-tuned to write code, which is helping students learn. The study wants to see how well these models work in education, if they can help with personalized learning, and what educators think about using them. |
Keywords
» Artificial intelligence » Gpt » Llama » Natural language processing » Nlp
