Fostering AI Literacy in Elementary Science, Technology, Engineering, Art, and Mathematics (STEAM) Education in the Age of Generative AI

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Abstract

The advancement of generative AI technologies underscores the need for AI literacy, particularly in Southeast Asia’s elementary Science, Technology, Engineering, Art, and Mathematics (STEAM) education. This study explores the development of AI literacy principles for elementary students. Utilizing existing AI literacy models, a three-session classroom intervention was implemented in an Indonesian school, grounded in constructivist, constructionist, and transformative learning theories. Through design-based research (DBR) and network analysis of reflection papers (n = 77), the intervention was evaluated and redesigned. Findings revealed clusters of interdependent elements of learner experiences, categorized into successes, struggles, and alignments with learning theories. These were translated into design moves for future intervention iterations, forming design principles for AI literacy development. The study contributes insights into optimizing the positive effects and minimizing the negative impacts of AI in education.

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Community structure in social and biological networks.

M Girvan, M. E. J. Newman (2002)

A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known--a collaboration network and a food web--and find that it detects significant and informative community divisions in both cases.