Unpacking Racism in Educational Models through a Polytextual Perspective: Leveraging ChatGPT for Deeper Insights

Potential Abstract:
This research study delves into the exploration of racism within educational models through the lens of a polytextual perspective, utilizing the innovative capabilities of ChatGPT. In the current landscape of education, the presence of racism and biases within instructional materials, curricula, and pedagogical approaches continues to be a pressing issue. Traditional methods of examining and addressing racism in education often fall short in capturing the complexity and nuances of these pervasive issues. This study seeks to bridge this gap by employing ChatGPT, a cutting-edge language model, to analyze and deconstruct various textual sources related to educational models and practices. By leveraging the polytextual nature of ChatGPT, the research aims to uncover hidden biases, stereotypes, and discriminatory practices embedded within educational content, shedding light on how these factors impact the learning experiences of students from diverse backgrounds.

The methodology involves feeding diverse sets of educational texts into ChatGPT and analyzing the generated outputs for biases, stereotypes, and subtle forms of racism. Through this process, the study aims to generate insights into the underlying mechanisms through which racism manifests in educational materials and models. The findings from this research have the potential to inform the development of more inclusive and equitable educational practices that address and mitigate biases effectively. Additionally, the study contributes to the broader conversation on leveraging AI technologies for promoting social justice and equity in education.

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