探索受提示大语言模型在教育与评估应用中的能力
Exploring the Capabilities of Prompted Large Language Models in Educational and Assessment Applications
AI总结：
本文通过教材出题、多语言MCQ生成、孟加拉语语法纠错及HR面试评估等任务，系统比较受提示LLM与人类专家的能力，揭示其在教育评估中的潜力与局限。
摘要：
在生成式人工智能（AI）时代，大语言模型（LLMs）的融合为现代教育领域的创新提供了前所未有的机遇。我们在教育与评估应用背景下开展对受提示LLMs的探索，以揭示其潜力。通过一系列精心设计的研究问题，我们研究基于提示的技术从中小学教材生成开放式问题的有效性，评估其从本科层次技术教材生成开放式问题的效率，并探索采用受思维链启发的多阶段提示方法进行语言无关的多项选择题（MCQ）生成的可行性。此外，我们以低资源印度语言孟加拉语的案例研究为例，评估受提示LLMs在语言学习方面解释孟加拉语语法错误的能力。我们还评估受提示LLMs评估人力资源（HR）口语面试转录文本的潜力。通过将LLMs在各类教育任务和领域中的能力与人类专家的能力并列比较，我们旨在阐明LLMs在重塑教育实践方面的潜力与局限。
英文摘要：
In the era of generative artificial intelligence (AI), the fusion of large language models (LLMs) offers unprecedented opportunities for innovation in the field of modern education. We embark on an exploration of prompted LLMs within the context of educational and assessment applications to uncover their potential. Through a series of carefully crafted research questions, we investigate the effectiveness of prompt-based techniques in generating open-ended questions from school-level textbooks, assess their efficiency in generating open-ended questions from undergraduate-level technical textbooks, and explore the feasibility of employing a chain-of-thought inspired multi-stage prompting approach for language-agnostic multiple-choice question (MCQ) generation. Additionally, we evaluate the ability of prompted LLMs for language learning, exemplified through a case study in the low-resource Indian language Bengali, to explain Bengali grammatical errors. We also evaluate the potential of prompted LLMs to assess human resource (HR) spoken interview transcripts. By juxtaposing the capabilities of LLMs with those of human experts across various educational tasks and domains, our aim is to shed light on the potential and limitations of LLMs in reshaping educational practices.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
