BIPED：面向ESL教育的教学知情辅导系统
BIPED: Pedagogically Informed Tutoring System for ESL Education
AI总结：
本文构建双语教学知情辅导数据集BIPED，归纳34种辅导者对话行为，并基于GPT-4和SOLAR-KO实现两步式CITS模型，验证其能复现人类教师风格并采用多样化教学策略。
摘要：
大型语言模型（LLMs）具有巨大潜力，可作为现成且成本高效的对话式智能辅导系统（CITS），用于教授英语二语学习者。然而，现有的CITS要么仅设计用于教授简单概念，要么缺乏应对多样化学习策略所需的教学深度。为了开发一个更具教学知情性、能够教授复杂概念的CITS，我们构建了一个名为BIPED（双语教学知情辅导数据集）的一对一、人与人英语辅导交互数据集。通过对辅导交互的事后分析，我们归纳出一套对话行为词典（34种辅导者行为和9种学生行为），并据此对收集的数据集进行进一步标注。基于一个两步框架——首先预测合适的辅导者行为，然后生成相应的回复——我们分别使用GPT-4和SOLAR-KO实现了两个CITS模型。我们通过实验证明，所实现的模型不仅复现了人类教师的教学风格，而且采用了多样化且符合语境的教学策略。
英文摘要：
Large Language Models (LLMs) have a great potential to serve as readily available and cost-efficient Conversational Intelligent Tutoring Systems (CITS) for teaching L2 learners of English. Existing CITS, however, are designed to teach only simple concepts or lack the pedagogical depth necessary to address diverse learning strategies. To develop a more pedagogically informed CITS capable of teaching complex concepts, we construct a BIlingual PEDagogically-informed Tutoring Dataset (BIPED) of one-on-one, human-to-human English tutoring interactions. Through post-hoc analysis of the tutoring interactions, we come up with a lexicon of dialogue acts (34 tutor acts and 9 student acts), which we use to further annotate the collected dataset. Based on a two-step framework of first predicting the appropriate tutor act then generating the corresponding response, we implemented two CITS models using GPT-4 and SOLAR-KO, respectively. We experimentally demonstrate that the implemented models not only replicate the style of human teachers but also employ diverse and contextually appropriate pedagogical strategies.
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。
