Reframing Roles in Education: Real-Time Utilization of Large Language Models in a Postmodern Context

Potential Abstract:
In this study, we examine the implications of utilizing large language models (LLMs) in real-time educational settings, particularly within a postmodern framework. As artificial intelligence technologies continue to advance, the integration of LLMs in educational contexts has become increasingly prevalent. However, the roles of teachers, students, and the technology itself are being redefined in this new landscape. Drawing on postmodern theories, we argue for a reframing of these roles to enhance learning experiences and outcomes. Through a qualitative analysis of classroom interactions, student feedback, and teacher reflections, we explore the ways in which LLMs influence the dynamics of teaching and learning in real-time scenarios. Our findings suggest that the use of LLMs not only transforms traditional educational practices but also challenges existing power dynamics and knowledge hierarchies within the classroom. We propose a reconceptualization of roles that embraces the collaborative nature of human-machine interactions, highlighting the potential for co-creation of knowledge and meaning. By integrating postmodern perspectives into the discourse on AI in education, this study contributes to a more nuanced understanding of the implications of LLMs on educational practices and pedagogies.

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