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Award ID contains: 1917673

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  1. ; ; (, National Council on Measurement in Education (NCME). https://aclanthology.org/2025.aimecon-main.13.pdf)
    Collaborative argumentation enables students to build disciplinary knowledge and to think in disciplinary ways. We use Large Language Models (LLMs) to improve existing methods for collaboration classification and argument identification. Results suggest that LLMs are effective for both tasks and should be considered as a strong baseline for future research. 
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    Free, publicly-accessible full text available October 27, 2026
  2. ; ; (, Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con))
    Collaborative argumentation enables students to build disciplinary knowledge and to think in disciplinary ways. We use Large Language Models (LLMs) to improve existing methods for collaboration classification and argument identification. Results suggest that LLMs are effective for both tasks and should be considered as a strong baseline for future research. 
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    Free, publicly-accessible full text available October 25, 2026
  3. ; ; ; ; (, Conference Proceedings of the 28th International Conference on Computational Linguistics)
    null (Ed.)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; (, Proceedings of The 12th Language Resources and Evaluation Conference (LREC))
    null (Ed.)
    Accepted Manuscript Full Text Available