Developing An Artificial Intelligence (AI) Agent for Early Elementary Education Using Dialectical Materialism

Abstract

Artificial Intelligence (AI) is becoming an essential tool in education, providing innovative solutions for personalized learning. However, early elementary education still needs AI agents to meet young children's unique cognitive and social needs. This study proposes the development of an AI agent based on the principles of dialectical materialism, which highlights the dynamic and interactive nature of learning.

This research aims to create an AI system incorporating socio-cultural and historical contexts into its algorithms, making it more adaptable and responsive to students' educational needs. The methodology involves designing an AI that uses qualitative analysis of classroom interactions and theoretical models to predict and adapt to individual learning trajectories.

This AI agent could significantly enhance engagement and learning effectiveness in early elementary students, especially in literacy and numeracy. By providing contextually relevant feedback and adapting to individual learning paths, the AI is expected to support academic performance and social development.

This research suggests that using dialectical materialism in AI design can lead to more effective educational tools that improve learning outcomes and support holistic child development. This proposal lays the groundwork for future studies to test and refine the AI agent in real-world educational settings.