arXiv 2406.139192024-09-25cs.AI

SPL：由大型语言模型驱动的苏格拉底式学习乐园

SPL: A Socratic Playground for Learning Powered by Large Language Model

Institute for Intelligent Systems, University of Memphis（孟菲斯大学智能系统研究所）；University of Memphis（孟菲斯大学）；School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学学院）；School of Psychology, Shaanxi Normal University（陕西师范大学心理学院）；School of Psychology, Central China Normal University（华中师范大学心理学院）；Department of Applied Social Sciences, Hong Kong Polytechnic University（香港理工大学应用社会科学系）

Liang Zhang, Jionghao Lin, Ziyi Kuang, Sheng Xu, Xiangen Hu

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AI总结：

研究引入由GPT-4驱动的智能辅导系统SPL，采用苏格拉底式教学法和提示工程促进多轮对话，初步实验表明其能提升个性化学习体验与批判性思维。

摘要：

基于对话的智能辅导系统（ITS）通过在交互式对话中自动化复杂的人类辅导策略，显著推进了自适应和个性化学习。然而，复制人类专家交流的精细模式在自然语言处理（NLP）中仍是一项挑战。NLP的最新进展，特别是OpenAI的GPT-4等大型语言模型（LLMs），通过基于广泛的预训练知识提供类人且具备上下文感知的响应，提供了有前景的解决方案。受LLMs在各种教育任务（如内容创作与摘要、问题解决和自动化反馈提供）中有效性的启发，我们的研究引入了Socratic Playground for Learning（SPL，苏格拉底式学习乐园），这是一个由GPT-4模型驱动的基于对话的ITS，采用苏格拉底式教学方法来培养学习者的批判性思维。通过广泛的提示工程，SPL能够生成特定的学习场景，并促进高效的多轮辅导对话。SPL系统旨在增强针对个人需求量身定制的个性化和自适应学习体验，特别专注于提升批判性思维能力。我们来自论文写作任务的初步实验结果表明，SPL有潜力改善辅导交互，并进一步增强基于对话的ITS功能。以SPL为例，我们的研究展示了LLMs如何增强基于对话的ITS，并扩大教育技术的可及性和有效性。

英文摘要：

Dialogue-based Intelligent Tutoring Systems (ITSs) have significantly advanced adaptive and personalized learning by automating sophisticated human tutoring strategies within interactive dialogues. However, replicating the nuanced patterns of expert human communication remains a challenge in Natural Language Processing (NLP). Recent advancements in NLP, particularly Large Language Models (LLMs) such as OpenAI's GPT-4, offer promising solutions by providing human-like and context-aware responses based on extensive pre-trained knowledge. Motivated by the effectiveness of LLMs in various educational tasks (e.g., content creation and summarization, problem-solving, and automated feedback provision), our study introduces the Socratic Playground for Learning (SPL), a dialogue-based ITS powered by the GPT-4 model, which employs the Socratic teaching method to foster critical thinking among learners. Through extensive prompt engineering, SPL can generate specific learning scenarios and facilitates efficient multi-turn tutoring dialogues. The SPL system aims to enhance personalized and adaptive learning experiences tailored to individual needs, specifically focusing on improving critical thinking skills. Our pilot experimental results from essay writing tasks demonstrate SPL has the potential to improve tutoring interactions and further enhance dialogue-based ITS functionalities. Our study, exemplified by SPL, demonstrates how LLMs enhance dialogue-based ITSs and expand the accessibility and efficacy of educational technologies.

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。