The Future of Education

Artificial intelligence systems increasingly shape scientific knowledge production, information access, and civic participation. Traditional digital literacy frameworks are insufficient for understanding algorithmically mediated environments, which requires a STEM-oriented approach to algorithmic literacy.

This study proposes and designs a community-based STEM education model implemented within public library settings. The model integrates elements of inquiry-based learning, critical data analysis, and applied AI literacy activities aimed at developing conceptual understanding of algorithms, data bias, and automated decision-making systems.

The instructional framework combines non-formal STEM education, problem-based learning scenarios, and facilitator-guided exploration of AI tools. A pilot implementation design is presented, including learning objectives, competency mapping, and evaluation indicators for measuring gains in algorithmic literacy.

The findings suggest that libraries can function as decentralized STEM learning hubs, extending science education beyond formal school environments and contributing to more equitable access to AI-related knowledge.

Keywords: Algorithmic Literacy; STEM Education; Artificial Intelligence; Science Teaching Models; Public Libraries