Reframing Educational Epistemologies through Innovative Applications of Large Language Models and Machine Learning

Potential Abstract: This research article explores the potential of innovative applications of large language models and machine learning techniques to reframe educational epistemologies. The rapid advancements in artificial intelligence have given rise to large language models, such as GPT-3, that possess the ability to generate human-like text and demonstrate a profound understanding of various domains. Integrating these models into educational settings offers opportunities to transform traditional modes of knowledge construction and dissemination. Drawing on the fields of artificial intelligence and education, this study investigates the theoretical and practical implications of using large language models in educational contexts to foster critical thinking, enhance student engagement, and promote inclusive pedagogies.

Through a comprehensive literature review, we examine the current state of research on large language models in education. We identify key theoretical frameworks, methodologies, and applications that have been explored thus far. By reframing educational epistemologies, we argue that large language models can serve as powerful tools to support a wide range of educational activities, including content creation, student feedback, personalized learning, and assessment. These models have the potential to enhance the quality and accessibility of education, particularly for students from marginalized communities.

Furthermore, we discuss the ethical considerations and challenges associated with the integration of large language models in education. These include issues of bias, privacy, and the need for human oversight. We explore potential strategies for mitigating these challenges and ensuring responsible use of these technologies in educational settings.

By synthesizing existing research and identifying gaps in the literature, this study aims to provide a foundation for future investigations in this emerging field. It offers insights into the potential benefits and limitations of incorporating large language models and machine learning techniques in educational practices, paving the way for evidence-based decision-making and pedagogical innovation.

Potential References: