Exploring the Interpersonal Dimensions of Generative AI Simulations in Androgogies: A Marxist Perspective

Potential Abstract:
This research article investigates the potential of generative artificial intelligence (AI) simulations in enhancing androgogical practices from a Marxist viewpoint, with a specific focus on the interpersonal dimensions involved. By leveraging the power of AI technologies, educational simulations have emerged as an innovative tool for experiential learning, allowing learners to engage in real-world scenarios and acquire skills through authentic experiences. However, the impact of these simulations on interpersonal dynamics and their alignment with the principles of androgogy remain understudied. This study aims to bridge this gap by critically examining the ways in which generative AI simulations can support androgogical practices while considering the social and economic implications through a Marxist lens.

Drawing upon theoretical frameworks rooted in androgogy and Marxist theory, this research employs a mixed-methods approach to explore the perceptions, experiences, and outcomes of learners engaging with generative AI simulations in various educational settings. The study will involve qualitative data collection through interviews and observations, as well as quantitative data collection using surveys and assessments. Through this comprehensive investigation, the research aims to provide insights into the potential benefits and challenges associated with using generative AI simulations in fostering interpersonal skills development, critical thinking, and social consciousness among learners.

The findings of this research will contribute to the existing literature by shedding light on the ways in which generative AI simulations can be harnessed to support androgogical principles while considering the implications for social justice and equity. By offering a Marxist perspective, this study will critically analyze the power dynamics, reproduction of social inequalities, and potential for emancipation within the context of generative AI simulations. The outcomes will inform both educators and AI designers on how to design and implement AI technologies that prioritize social and economic justice, promoting equitable access to educational opportunities.

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