OpineBot: Class Feedback Reimagined Using a Conversational LLM
Henansh Tanwar, Kunal Shrivastva, Rahul Singh, Dhruv Kumar
TL;DR
OpineBot reimagines class feedback by deploying a conversational LLM-based survey bot integrated with Semantic Kernel to deliver dynamic, personalized questions. In a mixed-methods study with 20 Operating Systems students in India, OpineBot elicited higher engagement and deeper feedback than traditional surveys, as evidenced by both quantitative metrics and qualitative interviews. The work demonstrates the feasibility of real-time, adaptive feedback collection at scale and highlights cognitive engagement as a key benefit. It also outlines limitations and avenues for extending the approach across courses, with considerations for ethics, UI, and instructor utility.
Abstract
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.