Towards Improving Teacher Performance Assessment through Human-Centered AI-Powered Survey Analysis
An Approach Using Large Language Models (LLM)
DOI:
https://doi.org/10.47756/aihc.y9i1.181Palabras clave:
Machine Learning, Large Language Models, Artificial Intelligence, Survey analysis, Text AnalysisResumen
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.
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Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
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