Unveiling Nuances: Leveraging Large Language Models for Open Educational Models through a Kristevan Lens

Potential Abstract: Large language models (LLMs) have emerged as powerful tools in various fields, including education. However, their potential for supporting open educational models and capturing nuances in educational content remains underexplored. This study leverages insights from Kristeva’s semiotic theory to examine how LLMs can be utilized to reveal and analyze subtle nuances in educational materials. By applying Kristeva’s notions of the symbolic, imaginary, and real in language, this research investigates the capacity of LLMs to enhance educational experiences by uncovering hidden meanings and underexplored perspectives. Through a series of qualitative analyses, we demonstrate how LLMs can augment traditional educational models by shedding light on diverse voices and perspectives, enriching the learning environment for students and educators alike. Our findings underscore the importance of integrating Kristevan frameworks into the design and implementation of LLM-based educational tools to promote a more inclusive and nuanced educational landscape.

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