Summary of Content Knowledge Identification with Multi-agent Large Language Models (llms), by Kaiqi Yang et al.
Content Knowledge Identification with Multi-Agent Large Language Models (LLMs)
by Kaiqi Yang, Yucheng Chu, Taylor Darwin, Ahreum Han, Hang Li, Hongzhi Wen, Yasemin Copur-Gencturk, Jiliang Tang, Hui Liu
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY)
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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 This paper proposes a novel approach to assessing teachers’ mathematical content knowledge (CK) in computer-aided asynchronous professional development systems. The authors tackle challenges faced by current automatic CK identification methods, such as diversity of user responses, limited annotated data, and low interpretability of predictions. They present a Multi-Agent LLMs-based framework, LLMAgent-CK, which utilizes strong generalization ability and human-like discussions to identify CK learning goals without requiring human annotations. The proposed method shows promising results on a real-world mathematical CK dataset, MaCKT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to help teachers improve their math skills online. It tries to solve problems with current methods that can’t handle different responses from users, don’t have enough good examples, and are hard to understand. The authors suggest using artificial intelligence models that work together to identify what math concepts teachers know or need to learn. They tested this approach on real data and found it worked well. |
Keywords
» Artificial intelligence » Generalization
