Unveiling Dynamic Voices in Education: A Naive Analysis of Large Language Models

Potential Abstract:
In recent years, large language models (LLMs) have gained significant attention in the field of education for their potential to enhance various educational practices. However, the extent to which these models can accurately capture the dynamic voices within educational settings remains largely unexplored. This study introduces a novel approach to analyzing LLMs through a “naive” lens, shedding light on the nuances and complexities of the voices that are often overlooked in traditional educational research. By applying this approach, we aim to uncover the hidden dynamics of diverse perspectives within educational texts and conversations. Drawing on a diverse range of educational data sources, including student essays, teacher feedback, and online discourse, this research seeks to provide insights into how LLMs can be harnessed to amplify marginalized voices and foster inclusivity in educational settings. Through a series of case studies and computational analyses, we demonstrate the potential of LLMs to reveal the intricate interplay of power dynamics, cultural nuances, and linguistic representations within educational contexts. This research contributes to the ongoing discourse on the ethical and practical implications of utilizing LLMs in educational research and practice, highlighting the importance of critically examining the ways in which these models shape and reflect the voices of diverse stakeholders.

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