Towards Improving Teacher Performance Assessment through Human-Centered AI-Powered Survey Analysis
Year: 2024
Type: article
Abstract: Practical evaluation is crucial for improving educational quality. The blind review conducts semester surveys among students at all academic levels within the institution for a comprehensive professor performance assessment. However, processing this data manually is time-consuming and restricts in-depth analysis. With the recent advancements in Large Language Models, such as OpenAI's GPT-4, chatbots can now accurately interpret nuanced survey responses. We propose using a chatbot powered by artificial intelligence and natural language processing, specifically large language models, to streamline survey analysis. This technology aims to quickly extract important insights, reduce staff workload, and enable informed decision-making. We are utilizing user-centered design methods to create and assess a prototype, ensuring that it meets the specific needs and characteristics of the users and provides an optimal user experience in the final product. This implementation will significantly improve operational efficiency and support continuous educational enhancement at the institution.
Institution: Universidad de Colima
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4408207303