SimPal：面向理解K-12物理教师教学目标的元对话框架研究
SimPal: Towards a Meta-Conversational Framework to Understand Teacher's Instructional Goals for K-12 Physics
AI总结：
针对现有对话式AI教学智能体预设教学目标难调整、与教师教学需求错位的问题，本研究提出元对话框架SimPal，通过自然对话识别教师教学目标并生成符号表示以适配原有智能体，在63个物理模拟上验证了其高准确度。
摘要：
仿真模拟在中小学科学教学中应用广泛，这类模拟通常配备对话式人工智能（AI）智能体，为使用模拟开展实验的学生提供实时脚手架支持。现有AI智能体均针对单个模拟高度定制，预设了一套固定的教学目标（Instructional Goals, IGs），教师难以调整教学目标，否则智能体可能与修订后的目标脱节。此外，出于同样原因，教师对采用新的第三方模拟持犹豫态度。本研究提出基于大语言模型（Large Language Model, LLM）的元对话智能体SimPal，以解决预训练对话式AI智能体与教师不断演进的教学方法之间的错位问题。教师通过与SimPal自然对话，首先说明其期望的教学目标，SimPal据此识别一组相关物理变量及其关联，构建目标教学目标的符号化表示。随后可利用这些符号化表示为原有AI智能体设计提示词，使其更好地匹配期望的教学目标。我们使用ChatGPT-3.5和PaLM 2两款大语言模型，在PhET和Golabz的63个物理模拟上对SimPal进行了实证评估。此外，我们利用TELeR分类法为教学目标识别相关物理变量，探究了不同提示工程技术对大语言模型性能的影响。结果表明，在使用定义完善的提示词时，SimPal能够以极高的准确度完成该任务。
英文摘要：
Simulations are widely used to teach science in grade schools. These simulations are often augmented with a conversational artificial intelligence (AI) agent to provide real-time scaffolding support for students conducting experiments using the simulations. AI agents are highly tailored for each simulation, with a predesigned set of Instructional Goals (IGs), making it difficult for teachers to adjust IGs as the agent may no longer align with the revised IGs. Additionally, teachers are hesitant to adopt new third-party simulations for the same reasons. In this research, we introduce SimPal, a Large Language Model (LLM) based meta-conversational agent, to solve this misalignment issue between a pre-trained conversational AI agent and the constantly evolving pedagogy of instructors. Through natural conversation with SimPal, teachers first explain their desired IGs, based on which SimPal identifies a set of relevant physical variables and their relationships to create symbolic representations of the desired IGs. The symbolic representations can then be leveraged to design prompts for the original AI agent to yield better alignment with the desired IGs. We empirically evaluated SimPal using two LLMs, ChatGPT-3.5 and PaLM 2, on 63 Physics simulations from PhET and Golabz. Additionally, we examined the impact of different prompting techniques on LLM's performance by utilizing the TELeR taxonomy to identify relevant physical variables for the IGs. Our findings showed that SimPal can do this task with a high degree of accuracy when provided with a well-defined prompt.
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。
