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A closer look at ChatGPT's role in concept map generation for education
2
Zitationen
4
Autoren
2025
Jahr
Abstract
Rapid progress in natural language processing (NLP) has ushered in a new era of artificial intelligence (AI) models. Large language models (LLMs), powered by deep learning algorithms, excel in understanding and generating coherent human language responses, revolutionizing various fields. In education, these models promise to enrich the learning experience by providing personalized support to students and facilitating communication between students and teachers. In particular, Generative Pre-trained Transformer (GPT) technology provides instant and contextually relevant responses, and it has become an increasingly interesting topic in education. This article explores the potential of ChatGPT to enhance learning activities in secondary schools, with a specific focus on concept maps, a valuable tool for fostering meaningful learning. Concept maps are widely recognized for their educational benefits but existing tools for automatic concept map generation lack flexibility and semantic understanding. In contrast, ChatGPT's adaptability and responsiveness make it a compelling candidate for generating concept maps tailored to individual needs. This paper sheds light on ChatGPT's potential in education and offers insights into its effectiveness in supporting learning activities through concept map generation. We present a study involving 83 secondary school students to test their perceptions of ChatGPT-generated concept maps. We compared concept maps created by both ChatGPT and teachers across six different topics. Using PlantUML, we standardized the concept maps, and then used various questionnaires to assess their quality, effectiveness, and impact on student performance. The statistical analysis revealed that the concept maps generated by ChatGPT were comparable in quality to those produced by teachers. This highlights the usefulness of ChatGPT in supporting structured learning activities while also significantly reducing the time and effort required to create concept maps.
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