Integrating Gamefied Algorithms and Large Language Models in Educational Settings: A Phenomenological Exploration of Praxis

Potential Abstract:
Abstract: This research article explores the integration of gamefied algorithms and large language models in educational settings, focusing on the phenomenological aspects of praxis. With the advent of artificial intelligence and the increasing availability of advanced technologies, there is a growing interest in leveraging these tools to enhance teaching and learning experiences. In particular, gamefied algorithms, which employ game mechanics and elements to engage and motivate learners, and large language models, such as deep neural networks, have shown potential in various domains. However, their application and implications in education remain underexplored.

This study adopts a phenomenological approach to investigate the lived experiences and perceptions of educators and students who engage with gamefied algorithms and large language models. Through in-depth interviews, observations, and document analysis, we aim to uncover the intricate dynamics and nuances of praxis in the context of this technological integration. By examining the experiences of those directly involved, this research seeks to shed light on the opportunities and challenges presented by these innovations and provide insights into their practical implementation.

The findings of this research will contribute to the understanding of the potential benefits and limitations of gamefied algorithms and large language models in education. Moreover, by adopting a phenomenological lens, this study aims to provide a more comprehensive and nuanced understanding of praxis in the context of these technologies. The insights gained from this research can inform the design and implementation of educational interventions using gamefied algorithms and large language models, promoting more effective and engaging learning environments.

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