Leveraging Distributed Hashing for Emancipatory and Critical Affordances in Collective Education

Potential Abstract:
With the increasing integration of artificial intelligence technologies in educational settings, there is a growing interest in exploring how these tools can be leveraged for emancipatory and critical purposes. This research investigates the potential of utilizing distributed hashing techniques to enhance the affordances of collective education practices. Distributed hashing, a method of distributing data across multiple nodes in a network, offers opportunities for decentralized collaboration and knowledge sharing within educational communities. By applying a critical lens to the design and implementation of distributed hashing mechanisms, this study seeks to empower learners and educators to engage in collaborative sensemaking, problem-solving, and knowledge production.

The research employs a mixed methods approach, combining quantitative analysis of data structures and algorithms with qualitative inquiry into the socio-cultural dynamics of educational communities. Through the analysis of real-world case studies and simulations, we aim to identify patterns of interaction, information flow, and power dynamics that influence the effectiveness of distributed hashing in supporting emancipatory and critical pedagogies. By examining the ways in which distributed hashing can facilitate equitable access to knowledge, promote diverse perspectives, and foster democratic decision-making processes, this study contributes to the ongoing discourse on the ethical use of AI in education.

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