Conventional class feedback systems often fall short, relying on static, unengaging surveys offering little incentive for student participation. To address this, we present OpineBot, a novel system employing large language models (LLMs) to conduct personalized, conversational class feedback via chatbot interface. We assessed OpineBot's effectiveness in a user study with 20 students from an Indian university's Operating-Systems class, utilizing surveys and interviews to analyze their experiences. Findings revealed a resounding preference for OpineBot compared to conventional methods, highlighting its ability to engage students, produce deeper feedback, offering a dynamic survey experience. This research represents a work in progress, providing early results, marking a significant step towards revolutionizing class feedback through LLM-based technology, promoting student engagement, and leading to richer data for instructors. This ongoing research presents preliminary findings and marks a notable advancement in transforming classroom feedback using LLM-based technology to enhance student engagement and generate comprehensive data for educators.

翻译：传统的课堂反馈系统往往存在不足，依赖静态、枯燥的调查问卷，缺乏激励学生参与的诱因。为解决这一问题，我们提出了OpineBot，这是一种采用大语言模型（LLMs）通过聊天机器人界面进行个性化、对话式课堂反馈的新颖系统。我们通过一项用户研究评估了OpineBot的有效性，研究对象为印度某大学操作系统课程的20名学生，利用问卷调查和访谈分析其体验。研究结果显示，与传统方法相比，参与者对OpineBot有压倒性的偏好，凸显了其在吸引学生、产生更深入反馈以及提供动态调查体验方面的能力。本项研究为进行中的工作，提供了初步结果，标志着基于LLM技术革新课堂反馈、促进学生参与度、为教师提供更丰富数据的重要一步。这项正在进行的研究展示了初步发现，代表了利用LLM技术提升学生参与度并为教育工作者生成全面数据，进而变革课堂反馈方面的一项显著进步。